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Record W3008937322 · doi:10.1093/jcag/gwz047.141

A142 ENDOSCOPIST EDUCATIONAL INTERVENTIONS ARE ASSOCIATED WITH IMPROVEMENTS IN ADENOMA DETECTION RATE: A SYSTEMATIC REVIEW AND META-ANALYSIS

2020· review· en· W3008937322 on OpenAlexaff
Enrique Moreno, Kirles Bishay, Natalia Causada Calo, Michael A. Scaffidi, Samir C. Grover, Nauzer Forbes

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsQueen's UniversityUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsColonoscopyMedicinePsychological interventionMeta-analysisColorectal cancerAdenomaMetric (unit)Colorectal cancer screeningSystematic reviewMEDLINESubgroup analysisInternal medicineCancerNursing

Abstract

fetched live from OpenAlex

Abstract Background Screening-related colonoscopy reduces the overall morbidity and mortality associated with colorectal cancer. In order for screening-related colonoscopy to be effective and safe, endoscopists must be well trained. However, a significant degree of variation exists between endoscopists in terms of adenoma detection rate (ADR) and cecal intubation rate (CIR). ADR in particular is an important colonoscopy quality metric that has been directly and inversely related to the rate of post colonoscopy colorectal cancer (PCCRC). Educational interventions aimed at endoscopists have been developed in an attempt to optimize the performance of colonoscopy. It is unknown what benefit these have on colonoscopy quality indicators or outcomes, if any. Aims We performed a systematic review and meta-analysis to determine whether there is an association between educational interventions aimed at endoscopists and improvements in colonoscopy quality indicators or outcomes. Methods An electronic search was conducted through August 2019 for studies reporting on targeted endoscopist educational interventions and associations with ADR or other colonoscopy quality indicators, or outcomes. Interventions such as hands-on training modules, skills enhancement courses were included Pooled rate ratios (RR) and weighted mean differences (WMD) were calculated using DerSimonian and Laird random effects models. A priori subgroup and sensitivity analyses were performed to assess for potential methodological or clinical factors associated with any of the outcomes of interest. Results From 2,253 initial studies, 14 were included in the systematic review, and 8 were included in the meta-analysis for ADR, representing 76,373 colonoscopies. Educational interventions were associated with improvements in ADR (RR 1.28, 95% confidence intervals, CI, 1.19–1.38). Educational interventions were also associated with improvements in overall polyp detection rate, PDR (RR 1.17, 95% confidence intervals, CI, 1.02–1.35). Educational interventions were not associated with longer withdrawal times (WMD -0.03 minutes, 95% CI, -0.57 - 0.51) or improved CIR (RR 1.00, 95% CI, 0.99 to 1.02), though unadjusted CIR was high in both the pre- and post-intervention groups, at 94.5% and 95.0%, respectively. Figure 1 shows Forest plots comparing pre-intervention and post-intervention rates for A) ADR, b) PDR and c) CIR. Conclusions Our study provides evidence that educational interventions aimed at endoscopists significantly improve ADR and overall PDR. Educational interventions did not impact withdrawal time or cecal intubation rates, and thus, the specific mechanisms for their benefit remain incompletely clear. As part of quality improvement programs to optimize colonoscopy performance, educational interventions should be considered. Funding Agencies None

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)0.012
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.032
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0180.049
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.040
GPT teacher head0.310
Teacher spread0.270 · 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.

Study designMeta-analysis
DomainMethods
GenreReview

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
Published2020
Admission routes1
Has abstractyes

Explore more

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