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Record W4391873766 · doi:10.1093/jcag/gwad061.071

A71 IMPACT OF THE COVID-19 PANDEMIC ON WAIT TIMES, AND CLINICALLY RELEVANT FINDINGS AT COLONOSCOPY

2024· article· en· W4391873766 on OpenAlexafffundabout
Alan Barkun, Kiana Ravanbakhsh, Daniel Kim, Gediwon Milky, P Stanowski, O Geraci, Myriam Martel, Charles Ménard, Daniel von Renteln

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2024
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de SherbrookeMcGill University Health Centre
FundersPartenariat Canadien Contre Le CancerMinistère de la Santé et des Services sociaux
KeywordsCoronavirus disease 2019 (COVID-19)PandemicColonoscopySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakMedicineIntensive care medicineVirologyEmergency medicineInternal medicineOutbreakDiseaseColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Background The widespread use of a standardized and validated province-wide colonoscopy referral form (PCRF), regrouping mutually exclusive indications into suggested priority wait times categories (P1 to P5), has allowed for a more comprehensive description of routine colonoscopy practice. Aims To better understand the impact of COVID-19 on the routine practice of colonoscopy. Methods This is a multicenter retrospective cohort study of consecutive adult patients referred with PCRF data available from two Quebec tertiary hospitals. Patient and procedural characteristics were recorded. The primary outcomes were the diagnostic rates of colorectal cancers (CRC) and clinically significant lesions (CSF), defined as endoscopic findings affecting subsequent patient management, excluding hemorrhoids and diverticulosis. The secondary outcome was procedural wait times. We compared endpoints contrasting colonoscopy findings pre-COVID (before March 15th, 2020) to intra-COVID (after April 15th, 2020). Results 7,476 pre-COVID and 7,181 Intra-COVID patients (mean age 59.2 ± 14.0 years, 50.9% female) were included from 2018 to 2022. There were no clinically relevant between-group differences in patient characteristics. CRC detection remained similar (0.9% pre- vs 0.8% intra-COVID, p=0.69), while CSF were diagnosed more frequently intra-COVID (41,2% vs 39.7%, p=0.02). There were higher rates of indications performed for urgent and semi-elective priorities (P2, P3) intra-COVID (2.9% vs 1.3%, pampersand:003C0.01, and 50.5% vs 47.9%, pampersand:003C0.01). Corresponding intra- vs pre-COVID differences in indications (all Pampersand:003C0.01) included a clinical suspicion of active inflammatory bowel disease (6.4% vs 5.2%), a high index of suspicion for cancer based on imaging, endoscopy or clinical exam (2.9% vs 1.3%), suspicion of occult colorectal cancer (1.4% vs 0.9%), and doing a repeat endoscopy because of a prior inadequate bowel preparation (1.3% vs 0.8%). In contradistinction, more elective colonoscopies had been performed pre-COVID (P4: 15.5% vs 8.6%, pampersand:003C0.01, and P5: 5.2% vs 3.8%, pampersand:003C0.01. COVID Colonoscopy wait times grew significantly longer intra- vs pre-COVID (176.3 ± 252.4 days vs 78.6 ± 110.2 days, pampersand:003C0.01). Conclusions We witnessed significant changes in indications and referral priorities distributions for colonoscopy pre- vs intra-COVID, amidst longer wait times. These practice modifications did not alter CRC diagnostic yields, but resulted in greater proportions of CSF detection 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)0.001
metaresearch head score (Gemma)0.004
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 score0.393
Threshold uncertainty score0.782

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.375
Teacher spread0.336 · 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
Published2024
Admission routes3
Has abstractyes

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