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Record W2792452884 · doi:10.1093/ecco-jcc/jjx180.920

P793 Prevalence and phenotype of inflammatory bowel disease across primary and secondary care: Implications for colorectal cancer surveillance

2018· article· en· W2792452884 on OpenAlexaboutno aff
Neel Heerasing, Peter Hendy, Lucy Moore, G Walker, Claire Bewshea, Tariq Ahmad, James Goodhand, Nicholas A. Kennedy

Bibliographic record

VenueJournal of Crohn s and Colitis · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseInternal medicineColorectal cancerUlcerative colitisPrimary sclerosing cholangitisPopulationColonoscopyDiseaseIncidence (geometry)ChromoendoscopyCrohn's diseaseCancerEnvironmental health

Abstract

fetched live from OpenAlex

Patients diagnosed with inflammatory bowel disease (IBD) with colonic involvement have increased risk of colorectal cancer (CRC). Colonoscopic surveillance reduces the risk of CRC-associated death through early detection; national/international guidelines recommend chromoendoscopy. We aimed to assess the burden of IBD in primary care unknown to our service and to identify patients eligible for, but not being offered, surveillance. We conducted a population-based observational study across primary and secondary care to evaluate the incidence and prevalence of IBD in our catchment area. Cases were identified from primary care using searches of practice databases and in secondary care using searches of electronic hospital records. Case inclusion required a specialist diagnosis of ulcerative colitis (UC), Crohn’s disease (CD) or IBD unclassified and confirmatory evidence (specialist correspondence, histology, endoscopic or operative findings). IBD was phenotyped according to the Montreal Classification and patients under the age of 75 years, who had been diagnosed with IBD with colonic disease involvement for more than 10 years, were deemed eligible for colonoscopic surveillance. Patients from 48/49 GP practices within our catchment area were included. We identified 2816 patients with IBD living within our catchment of which 11% (309/2816) were unknown to any local secondary care service. Overall, UC prevalence was 435/100000 people (95% confidence interval 416–456), Crohn’s disease prevalence was 231/100000 (95% CI 216–246), and IBD unclassified prevalence was 31/100000 (95% CI 26–37). Patients managed solely in primary care, compared with those in secondary care, were older (median age [IQR] 63.2 [50.7–72.3] vs 54.2 [40–68.1] years, p < 0.0001) and had longer disease duration (median [IQR] 23.3 [13.6–36.7] vs 11.6 [5.6–20.6] years, p < 0.0001). The proportion of UC out of total IBD was higher in primary care (73% [227/309] vs. 61% [1531/2507], p < 0.0001). A higher proportion of IBD patients in primary care than secondary care had undergone a colectomy (17% [54/309] vs 28% [148/2507], p < 0.0001). Overall, 16% (393/2507) patients known to secondary care and 30% (92/309) of patients unknown to any secondary care services were eligible for colonoscopic surveillance, equivalent to approximately one colonoscopy list per week. We report one of the highest prevalence rates of IBD in Western Europe (1 in 143 patients). 11% of patients living in our immediate catchment area were unknown to our service; a third of these were eligible for colonoscopic colorectal cancer surveillance. Effective colorectal cancer surveillance programmes in IBD must target primary-care populations and not just known secondary care populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.260
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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