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Record W4407285122 · doi:10.1093/jcag/gwae059.088

A88 PATIENT-CENTRIC VALIDATION OF A PROVINCE-WIDE COLONOSCOPY REFERRAL SHEET DETERMINING WAIT-TIME PROCEDURAL ALLOCATION

2025· article· en· W4407285122 on OpenAlexaff
Alan Barkun, Saro Aprikian, Christopher Hansen-Barkun, Dong Hyun Danny Kim, Gediwon Milky, J Beauchesne-Blanchet, Myriam Martel, Charles Ménard, Daniel von Renteln

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2025
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversité de MontréalUniversité de SherbrookeMcGill University Health CentreMcGill University
Fundersnot available
KeywordsReferralColonoscopyMedicineMedical emergencyMedical physicsFamily medicineColorectal cancerInternal medicineCancer

Abstract

fetched live from OpenAlex

Abstract Background The widespread use of a standardized, validated province-wide colonoscopy referral form (PCRF), regrouping mutually exclusive indications into suggested priority wait time categories has allowed for a more comprehensive, patient-centric assessment of routine colonoscopy practice Aims To better characterize colonoscopic finding yields based on specific clinical indications (PCRF) determined by patient symptoms’ questionnaire-generated indications rather than referring physician-based indications. We also attempt further validation of the PCRF. Methods Prospective cohort of consecutive adult patients from two hospitals. Information collected from PCRF (therefore referring physician assessment) but also additional symptoms from a patient questionnaire. The primary outcome was the colonoscopy findings. Descriptive and inferential statistics and multivariable regression analyze predictive modeling of different indications and symptoms. Results Overall, 5979 patients (mean age 59.2±14.2years, 49.8% female, mean BMI 26.7±5.2) were included from June 2022 to February 2024. Duration between colonoscopy referral and colonoscopy (days) was 266.7 ± 405.9 (median=138 days). Excluding diverticulosis and non-bleeding hemorrhoids, 41.3% of patients had clinically significant lesions and 0.9% adenocarcinomas. The main indication according to patients was surveillance of polyps (IN13 - 35.7%), surveillance for significant family history (IN21 - 21.2%), IBD surveillance (IN15 - 11.7%). Based on additional questions of the patient questionnaire that did not correspond to any indication on the PCRF, 70.0% had no additional symptoms while 13.3% experienced abdominal pains, and 11.1% cramping, with 4.8% reporting weight loss. Between-group comparisons for adenocarcinoma and clinically significant lesions are described in figure 1. Models with the best fit for predicting adenocarcinoma were associated with the combinations of age>40, and presence of IN2, IN5, IN6 and IN17 with OR=7.74 (4.45; 13.46) (or OR=6.46 (2.78; 15.14) for age>60). Clinically significant lesions were associated with the combinations of age>60, IN5, IN17 and IN13 with OR=2.05 (1.83; 2.90). Conclusions This exercise has allowed further validation of recently adopted provincial changes in PCRF priorities attributed to some indications. Addition of symptoms not captured in PCRF indications did not improve prediction using existing indications in multivariable modelling. The use of multiple PCRF indication in allocating waiting priorities rather than choosing the sole perceived most urgent one (which is how the PCRF is used) now requires prospective validation. Funding Agencies CPAC and MSSS

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)0.008
metaresearch head score (Gemma)0.020
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 score0.998
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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