Diagnostic intervals before and after implementation of cancer patient pathways – a GP survey and registry based comparison of three cohorts of cancer patients
Jensen et al. BMC Cancer
Diagnostic intervals before and after implementation of cancer patient pathways - a GP survey and registry based comparison of three cohorts of cancer patients
Henry Jensen 0 1 3 4
Marie Louise Trring 1 4
Frede Olesen 1 4
Jens Overgaard 2
Morten Fenger-Grn 1 4
Peter Vedsted 1 4
0 Section for General Medical Practice, Department of Public Health, Aarhus University , Bartholins Alle 2, DK-8000 Aarhus C , Denmark
1 Research Centre for Cancer Diagnosis in Primary Care, Research Unit for General Practice, Department of Public Health, Aarhus University , Bartholins Alle 2, DK-8000 Aarhus C , Denmark
2 Department of Clinical Medicine - Department of Experimental Clinical Oncology, Aarhus University Hospital , Noerrebrogade 44, DK-8000 Aarhus C , Denmark
3 Section for General Medical Practice, Department of Public Health, Aarhus University , Bartholins Alle 2, DK-8000 Aarhus C , Denmark
4 Research Centre for Cancer Diagnosis in Primary Care, Research Unit for General Practice, Department of Public Health, Aarhus University , Bartholins Alle 2, DK-8000 Aarhus C , Denmark
Background: From 2008, Danish general practitioners could refer patients suspected of having cancer to standardised cancer patient pathways (CPPs). We aimed to compare the length of the diagnostic interval in 2010 with the length of the diagnostic interval before (2004/05) and during (2007/08) the implementation of CPPs in Denmark for all incident cancer patients who attended general practice prior to the cancer diagnosis. Methods: General practitioner questionnaires and register data on 12,558 patients were used to compare adjusted diagnostic interval across time by quantile regression. Results: The median diagnostic interval was 14 (95% CI: 11;16) days shorter during and 17 (95% CI: 15;19) days shorter after the implementation of CPPs than before. The diagnostic interval was 15 (95% CI: 12;17) days shorter for patients referred to a CPP in 2010 than during the implementation, whereas patients not referred to a CPP in 2010 had a 4 (95% CI: 1;7) days longer median diagnostic interval; the pattern was similar, but larger at the 75th and 90th percentiles. Conclusion: The diagnostic interval was significantly lower after CPP implementation. Yet, patients not referred to a CPP in 2010 tended to have a longer diagnostic interval compared to during the implementation. CPPs may thus only seem to expedite the diagnostic process for some cancer patients.
Diagnostic interval; Urgent referral; (early) diagnosis; Cancer; Primary care; Cohort; Denmark
Standardised cancer patient pathways (CPPs) have been
introduced in some countries [1-8]. Even though CPP
contents differ between countries, they all operate with a
guaranteed timeframe for timely diagnosis. After years
of increasing waiting times for cancer patients in
Denmark, the Danish government and the Danish regions
(i.e. hospital owners) declared in 2007 that cancer should
be diagnosed and treated without delay . Consequently,
the Danish government and the Danish National Board
of Health (today: the Danish Health and Medicines
Authority) introduced CPPs in Denmark in 2008 .
CPPs were introduced in Denmark under the assumption
that timely diagnosis and decisions on treatment options
could be enhanced, psychosocial distress limited, and
ultimately improve the prognosis for cancer patients.
The Danish CPPs are standardised pathways for the time
up to the final diagnosis and the start of treatment
comprising well-defined sequences and time frames
for diagnostic procedures and treatments for patients
fulfilling CPP access criteria. The Danish CPPs were
accompanied by renewal and expansion of equipment
for imaging and radiotherapy. Patients can be referred
to a Danish CPP when the clinician has a reasonable
suspicion of cancer as the final diagnosis .
A key phase in the cancer journey is the diagnostic
interval (DI), i.e. the time from the patients first presentation of
symptoms in the health care system (usually in primary
care) until diagnosis . Despite a sparse body of evidence
, mortality has been shown to increase with longer DI
among patients with colorectal, lung, breast, melanoma or
prostate cancers . The DI is important as it measures
the timeliness of the health-care system as a whole across
Few studies have addressed the possible CPP impact
on DI length. Most of these studies have primarily
focussed on selected parts of the DI for specific cancer
types, or studies have been performed with no baseline
measures [1,4-7]. In addition, some studies are restricted
to include only patients with predefined symptoms of
cancer  or exclusively patients referred for a CPP 
although some studies have reported that fairly few cancer
patients initially present with well-known symptoms
of cancer that allow direct access to a CPP [14,15].
Furthermore, many of the patients who are not referred to
diagnostic workup through a CPP experience longer
time intervals [7,16-18]. Thus, the possible effect of
CPP implementation might vary with the actual use
of CPPs, patient symptomatology and cancer types.
Therefore, we aimed to compare the length of the
diagnostic interval in 2010 with the length of the diagnostic
interval before (2004/05) and during (2007/08) the
implementation of CPPs in Denmark for all incident
cancer patients who attended general practice prior to
the cancer diagnosis and for the five most common cancer
types, regardless of the patients presenting symptoms.
Data from GPs and registries from the Danish Cancer in
Primary Care (CaP) cohort  was used in an ecological
design to compare three cohorts of incident cancer
patients before, during and after CPP implementation in
order to investigate the impact of the CPP implementation
in 20072009  as a natural experiment. The ecological
design was a consequence of the unknown joint
distribution of CPP referral prior to the CPP implementation .
The study took place in Denmark, where the annual
cancer incidence rate is 326 per 100,000 . The
Danish publicly funded healthcare system ensures a
uniform healthcare system with free access to diagnostics
and treatment for all citizens. More than 98% of all citizens
are registered with a specific general practice, which they
may consult for medical advice. The Danish general
practitioners (GPs) act as gatekeepers to the rest of the
health care system. During the study period, 78.6% of all
cancer patients in Denmark had been diagnosed through
a primary care route .
Patient population and data collection
Identification of patients, data collection and response
analysis were based on the large Cancer in Primary Care
study, which has been described in detail elsewhere .
In brief, we identified incident cancer patients aged 18 years
or above and listed with a Danish GP and for whom
diagnoses were coded according to the International
Classification of Diseases, version 10 (ICD-10), i.e.
C00.0-C99.9 (except for non-melanoma skin cancer
(C44)), before from the former Danish County of
Aarhus (640,000 inhabitants) (1 September 2004 31
August 2005), during from the Region of Southern
Denmark and the Central Denmark Region (2.1 million
inhabitants) (1 October 2007 30 September 2008) and
after, from entire Denmark (1 May 31 August 2010), the
implementation of the CPPs at national level. Patients
were identified through the Patient Administrative
Systems (PAS) of the Danish hospitals and the Danish
National Patient Registry. A patient was defined to have
incident cancer when cancer was registered as the primary
diagnosis during one of the inclusion periods, and no
prior history of cancer was recorded. The history of cancer
was checked in the Danish Cancer Registry . We
excluded 570 out of 22,736 patients (3%) because
their diagnosis could not be verified by the Danish
For each sampled cancer patient, data from the registered
GP was collected by a questionnaire, which was sent
to the GP 25 weeks after identification of the patient.
Participating GPs were asked to fill in the questionnaire
on the basis of the information in their electronic medical
records. Non-responders received a reminder after 35
weeks. Information from the questionnaire was combined
with data from the Danish Cancer Registry to ensure that
we obtained a validated date of diagnosis .
Figure 1 shows the patient flow in the present study.
In 4,603 (20.8%) of the 22,169 verified cases, the invited
GPs did not respond (GP response rate: 79.2%). Patients
with responding GPs did not differ from patients with
non-responding GPs in regard to 1-year survival,
comorbidity or educational level. However, patients listed with
responding GPs were more likely to be women, younger, to
have a higher disposable income, to have more regionally
or distantly spread tumours, and correspondingly more
likely to have breast cancer and less likely to have prostate
cancer than patients from non-responding GPs. These
differences were small and clinically irrelevant, but
were statistically significant due to the sample size
(data published elsewhere) . We excluded 3,766
(21.4%) patients from this study as the GP stated that (s)he
was not involved in the diagnosis; i.e. patients diagnosed
through screening, emergency access or as coincidental
findings during diagnosis of other illnesses. We also
excluded 15 male breast cancer patients (0.1%) (Figure 1).
Figure 1 Flowchart for cancer patients. Boxes on the left indicate exclusion of patients who did not meet the inclusion criteria and boxes on the
right indicate drop-outs due to non-response and missing data.
Outcome, exposure and possible confounders
In accordance with the Aarhus Statement, we defined
the primary outcome, the DI, as the time from when the
patient made the first symptom presentation to a GP
until the time of diagnosis . The DI was calculated
by using the GP questionnaire to obtain the date of the
patients first presentation of symptoms to the GP and the
Danish Cancer Registry to define the date of diagnosis.
The date of diagnosis recorded in this registry corresponds
to the date of first contact (admission date) with the
hospital department at which the cancer diagnosis
was first registered as the primary cause of contact. If
the patient was diagnosed by a private practicing
specialist, the date of diagnosis corresponds to the date
of the clinical diagnosis . If the date of diagnosis
was missing in Danish Cancer Registry, the admission
date recorded in the Danish National Patient Registry
Exposure was defined as implementation of CPP,
and each of the three sub-cohorts was treated as an
independent exposure group: 2004/05 = no CPPs
implemented (before), 2007/08 = CPPs under implementation
(during) and 2010 = fully implemented CPPs (after).
Subsequently, we subdivided the after group into two
groups: patients who were initially referred to a CPP
(after-CPP) and patients who were not (after-no CPP).
Possible confounders accounted for were gender,
age, comorbidity, educational level and disposable
income. Gender and age were derived from the
patients Danish civil registration number . We
computed a Charlson Comorbidity Index score according
to the method described by Quan et al.  using
the date of the patients first consultation with the GP
as the index date. We grouped the comorbidity scores
into none (no recorded disease), moderate (score of
1 or 2), and high (score of 3 or more). We grouped
educational levels according to the International
Standard Classification of Education (ISCED)  into
low (ISCED levels 1 and 2), medium (ISCED levels
3 and 4) and high (ISCED levels 5 and 6), and we
categorized disposable household OECD income in
the year prior to the diagnosis into tertiles (low,
medium and high).
The study was approved by the Danish Data Protection
Agency (file. no. 2009-41-3471). The Danish National
Board of Health gave legal permission to obtain
information from the GPs medical records. The study did not
require approval from the Committee on Health Research
Ethics of the Central Denmark Region as no biomedical
intervention was performed.
We analysed changes in the DI for all cancers combined
and for each of the five most common cancer types in
Denmark: colorectal, lung, malignant melanoma, breast
and prostate cancer .
All statistical analyses, except for analyses of missing
data and sensitivity, were restricted to complete cases
(i.e. GPs who completed the questionnaire and who were
also involved in the diagnostic process).
Prefatory comparisons of the three sub-cohorts were
performed using non-parametric methods: The Chi2 test
was used for categorical data, while the Wilcoxons rank
sum test was used for continuous data.
We used the qcount procedure written by Miranda
 for the quantile regression analyses  on the
smoothed quantiles to estimate the adjusted differences
in the diagnostic interval at different percentiles; analysis
on the smoothed quantiles are recommended for analyses
of discrete (count) data . Two adjusted models were
considered: a model with no regard of referral route
(overall trend) and a model with patients after the
CPP implementation in 2010 divided into referral
routes (trend by referral route). We adjusted for gender,
age, cancer site, comorbidity, educational level and
disposable income in both models. Age was centred
at 45 years of age and was entered into the models as a
continuous variable, while the other known confounders
were entered as categorical variables.
To investigate the implication of missing data of
DI, we performed best/worst case scenario sensitivity
analyses by assigning the value 0 (best case) and the
maximum values for the sub-cohorts (worst case) of
the diagnostic intervals.
A statistical level of p 0.05 was considered significant
in all analyses. Analyses were done using Stata statistical
software, version 13 (StataCorp LP, College Station,
Demographic characteristics of excluded and included
In total 13,785 patients fulfilled the inclusion criteria. We
excluded 1,227 patients (8.9%) due to missing information
of the DI. These patients were more likely to be women,
younger than 45 years of age or older than 75 years of age,
to be diagnosed with breast or prostate cancers, to have
high income or to have higher survival rates than the
included patients. The characteristics of the analysed
12,558 are presented in Table 1.
Diagnostic interval overall tendency
The unadjusted diagnostic intervals before, during and
after CPP implementation are summarised in Table 2
and Figure 2. The median DI was statistically significantly
lower across time: 49 (interquartile interval (IQI): 24;96)
days before, 35 (IQI: 16;78) days during and 32 (IQI:
14;73) days after CPP implementation (Table 2 &
Figure 2). The overall result remained the same when
we adjusted for differences between populations; the
median DI was 14 (95% CI: 11;16) days shorter during the
transition stage than before CPP implementation and 17
(95% CI: 15;19) days shorter after CPP implementation
(Table 3). Compared to the period before CPPs, the DI
was shorter both during and after CPP implementation
for all cancer types, although not statistically significant at
all percentiles (Additional file 1).
Diagnostic interval by referral route compared to before
When patients diagnosed after CPP implementation
were categorised according to the GPs use of CPP 62.8%
were categorised as non-CPP referrals. The unadjusted
median DI was lower for both the after-CPP group and the
after-no CPP group compared to before the
implementation of CPPs (p < 0.001) (Table 2). The DI was significantly
longer for the after-no CPP group than for the after-CPP
group (p < 0.001). This was observed for all the five major
cancer types (Table 2). The 75th percentile was 91 days in
2010 for the after-no CPP group compared to 44 days for
the after-CPP group.
For the after-CPP group, the adjusted median was 23
(95% CI: 21;25) days shorter than before the
implementation. For the after-no CPP group, the adjusted median
was 9 (95% CI: 7;12) days shorter than before the
implementation. At the 90th percentile, the DI for the after-CPP
group was 110 (95% CI: 67;153) days shorter than
before, while similar (6 (95% CI: 66;77) days shorter)
for the after-no CPP group than before (Table 3).
This tendency was observed for all five major cancer
types, although not statistically significant at all percentiles
(Additional file 1).
Diagnostic interval by referral route compared to during
For the after-CPP group, the adjusted median DI was
15 (95% CI: 12;17) days shorter than during the
implementation. For the after-no CPP group, the
adjusted median DI was 4 (95% CI: 1;7) days longer
than during the implementation. Likewise, at the 90th
percentile, the DI for the after-CPP group was 80
(95% CI: 34;126) days shorter than during the
implementation, while the DI for the after-no CPP group
was insignificantly 48 (95% CI: 49;145) days longer
than during the implementation (Table 3). This
tendency was observed for all cancer types separately,
although not statistically significant at all percentiles
(Additional file 1).
Age groups (years):
Educational level ISCED
Disposable Income OECD (EURO)
Column five and six display the after cohort divided by referral route (referred to a Cancer Patient Pathway (after-CPP) or not (after-no CPP)).
The sensitivity analyses did not alter the overall
results as the median DI was still lower both during
and after the implementation compared to before;
this was found for both worst and best case scenario.
Furthermore, both scenarios displayed that the median
DI was longer for the after-no CPP group than for
the after-CPP and also showed that the after-no CPP
group tended to experience longer DIs after than
during CPP implementation. Sensitivity analyses
restricted to the same geographical region showed similar
We found that the median length of the DI in Denmark
was shorter after the CPP implementation (in 2010) than
before the CPP implementation (in 2004/05); the largest
difference was found between the period before the
CPPs (2004/05) and during the implementation phase in
2007/08. Furthermore, the largest difference in DI
(compared to before the implementation) was found
among patients in the after-CPP group, whereas patients
in the after-no CPP group had only a minimally lower DI
(compared to before the implementation) and still a long
DI. In fact, the after-no CPP group in 2010 displayed a
Table 2 The unadjusted diagnostic interval (DI) shown before (2004/05), during (2007/08) and after (2010 combined)
the implementation of CPPs (N=12,558)
75 years and above
Disposable income OECD
Educational level ISCED
Column five and six display the after cohort divided by referral route (referred to a Cancer Patient Pathway (after-CPP) or not (after-no CPP)).
longer median DI than during the implementation.
Finally, we found that the 90th percentile DI for the
after-no CPP group did not differ from before the
CPP implementation. Hence, only patients in the CPP
group in 2010 had a lower diagnostic interval after the
CPP implementation than before CPP.
This finding must be compared to the fact that 63% of
all Danish cancer patients in 2010 were not initially
referred to a CPP . Hence, the majority of cancer
patients did not experience a lower DI across the
investigated time period. This may have major impact
on the prognosis for both patients with long DIs and
at a population level, as it is reasonable to assume
that expedited diagnosis of symptomatic cancer is
likely to benefit the patients in terms of improved
survival [12,31-35]. Hence, reductions in the diagnostic
intervals (as we have shown) may influence the cancer
stage distribution and hence survival at population level;
these relations have been claimed to partly explain the
improvement in survival among Danish cancer patients
[36,37]. However, there is not yet enough evidence to
substantiate this argument. Another equally important
Figure 2 Cumulative frequencies of diagnostic interval (DI) before, during and after CPP implementation in Denmark. DI ranked in order and
depicted by a Lorenz diagram. DI of longer than 365 days omitted for illustration purposes.
effect of reducing the diagnostic interval is that it should
improve patient satisfaction and limit psychological
distress among cancer patients, which was another
important aim of the CPPS .
The literature on DI is sparse and direct comparisons
between studies and countries are difficult . We
know of only two studies that have investigated the DI
across time periods in connection with implementation
of CPPs: a Danish study on head and neck cancer 
and the large UK study on the implementation of the
NICE guidelines . Our results, which show a shorter
DI after CPP implementation, are in line with the
shorter DI found by these two studies. Yet, our study is
the first to quantify adjusted changes in the DI across
time at different percentiles. Our findings display that
the decrease in the DI across time was largest among
the patients who waited the longest, which may have
major impact on the prognosis. Furthermore, we were
able to quantify the changes in the DI across time by use
of CPPs. We found that the patients referred to a CPP
have a significantly shorter DI than patients not referred
to a CPP at all percentiles. These findings are in line
with the results of our previous studies in which we also
accounted for the patients symptom presentation .
The design of the study does not permit us to infer
causality between the implementation of CPPs and
the lower DI seen across time. A number of changes
in policy, clinical practice and investments may have
After vs. before
Estimate (95% CI)
contributed to these changes in DI across time. The
implementation of CPPs was just one of many new
initiatives in the second Danish cancer plan, which
also promoted a huge expansion in radiotherapy facilities
. As the largest difference in DI was observed from
before to during the implementation of CPPs, most of the
differences found can probably not be attributed to
the (full) implementation of the CPPs. The decision
to implement CPPs was taken in August 2007, just after a
declaration by the prime minister and the Danish National
Board of Health that cancer should be treated without any
delay in Denmark . Therefore, local leaders may have
started to streamline the diagnostic trajectories already
before the official implementation in 2008/09, which
could have contributed to the lower DIs observed in
2007/2008. Our study further shows that the general
tendency towards lower diagnostic interval after the full
implementation of the CPPs was different between patient
referred to a CPP and patients who were not.
The results found in the study together with other
findings of longer intervals among patients not referred
to rapid diagnostics [16,17] supports the argument that
introduction of CPPs only benefit patients referred to a
CPP . This has been suggested to be due to that the
fast-track system may disadvantage the large group of
patients in whom the first appearance of disease does
not involve significant cardinal symptoms of cancer
[39,40]. This is underlined by the different proportion of
CPP referrals between cancer types with e.g. breast
cancer most often referred to a CPP . Our findings
may thus be interpreted as a demonstration of the possible
danger of considering standardised CPPs as stand-alone
referral routes for cancer. Our results may also indicate a
need for an additional approach to ensure fast diagnosis of
cancer, for instance by providing quick and easy access
from primary care to all initial investigations ordered by a
GP to establish the possibility of cancer .
Strengths and weaknesses of the study
The main strength of this study is the study population,
which was well-defined and complete with minimal
selection bias [19,41]. We may have missed some patients,
but this risk is expected to be negligible as we also
included late-registered patients . Another major
strength that decreases the risk for selection bias is that
we included all cancer patients, regardless of symptom
presented at first contact and cancer site.
The Danish health care system is almost uniformly
organized across different geographically and
administratively independent regions. This organization allowed the
merging of the three sub-cohorts into one, although they
originated from partly overlapping geographical locations
(regions) in Denmark and thus belonged to different
subsets. In fact, the case-mix of the sub-cohorts resembles
the case-mix in the DCR of a given year [41,42]. This
indicates that the identified incident patients in the CaP
Cohort are representative of incident cancer patients in
Denmark at the time when the patients were identified.
The considerable size of the study ensures statistical
precision, and the high response rate of 79% reduces the
risk of selection bias. However, patients who were not
included may have had longer DIs than the included
patients. We believe this is not associated with the
implementation of CPPs and, therefore, would not bias
the observed DI differences between the sub-cohorts.
Nevertheless, selection bias may still be present, but as
our sensitivity analyses showed no impact on the results,
this possible bias has been considered to be negligible
(if present at all).
Information bias due to GP recall bias was reduced by
using the GPs contemporaneously updated electronic
medical records. Even so, the retrospective nature of the
questionnaire holds the risk that the GPs may have
misinterpreted the date of first presentation of symptoms
for some of the cases. We believe that this possible risk is
equal for all sub-cohorts and consequently will not bias
the DI differences between the cohorts.
For obvious reasons, it is not possible to identify the
patients in the before and during cohort, who would
have been referred to CPPs had they been implemented.
It is likely, that patients not eligible for CPPs would have
had a tendency to longer DIs before and during the
implementation. Hence, the fact that patients not
referred to CPP have longer DI than the during cohort as
a whole, cannot be rigorously interpreted as a causal effect
of CPPs disadvantaging this group. Furthermore, as the
categorisation of patients according to CPP or not was
based on the GPs choice of referral, comparison with
all patients diagnosed at hospitals using CPPs must
The use of the date of first contact to a hospital ward
as the date of diagnosis would tend to underestimate the
length of the DI. This standard procedure is caused by
the Danish Cancer Registry as the first contact date is
recorded in this register as the date of diagnosis, even
though most diagnoses are verified after this date
(mostly at a multidisciplinary team meeting at the
hospital). We consider this to be non-differential as we
suspect that it is not associated with the CPP
implementation, and hence deviations in date of diagnosis alone
cannot explain the observed differences in DI between the
cohorts. Yet, if this information bias may have been
stronger for patients not referred to a CPP (after-no CPP
group) as these patients have longer intervals and thereby
may have raised the possibility that the date of diagnosis
was moved relatively more than for the other groups of
patients, this bias could have led to an underestimation of
the observed DI difference between the after-no CPP
group and the other groups. Our observed differences
would thus be minimum estimates of the true differences.
The diagnostic interval for the five most common
cancers and for all cancers combined was lower in
Denmark in 2010 than in 2004/05. The largest difference
was seen from 2004/05 to 2007/08. Patients who were not
referred to a CPP in 2010 still had long diagnostic
intervals and tended to have a longer diagnostic interval
than patients in 2007/08 when the CPP was not fully
implemented. The patients with the 10% longest waiting
time and who were not referred to a CPP in 2010 actually
displayed a DI similar to the DI for the 10% waiting the
longest in 2004/05.
These findings suggest that, despite the good
intentions with implementing the CPPs, patients who were
not referred to a CPP seem not to have gained faster
diagnosis as these patients tend to have similar diagnostic
intervals as before the implementation of CPPs. This
demonstrates a need for more focus on providing
faster diagnostic pathways for the large groups of
patients who are not referred to a CPP in the initial
phases of their disease.
Additional file 1: Estimated differences in diagnostic intervals (DIs)
after and during CPP implementation compared to before, by
CPP: Standardised Cancer Patient Pathways; GP: General practitioner; CI: 95%
Confidence Interval; CCI: Charlson Co-morbidty Index; ISCED: International
Standard Classification of Education.
The authors declare to have no competing interests.
HJ was involved in the conception of the study, participated in the studys
design, performed the statistical analyses and drafted the manuscript.
MLT, FO, JO and PV all contributed to the conception, development and
design of the study and provided critical revision of the intellectual contents
of the manuscript. MFG contributed to the conception of the study and the
statistical analysis and provided critical revision of the intellectual contents of
the manuscript. All authors have read and approved the final manuscript.
We would like to thank data manager Kaare Rud Flarup for his outstanding
and meticulous help in setting up and maintaining the database and
enabling register linkage at Statistics Denmark. We thank Statistics Denmark
for providing the data platform and the secure data environment.
This study was funded by the Novo Nordisk Foundation, the Danish
Cancer Society, the Health Foundation (Helsefonden), the Danish foundation
Trygfonden and the Central Denmark Region Foundation for Primary Health
Care Research (Praksisforskningsfonden).
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