Interventions to improve the quality of screening-related colonoscopy: protocol for a systematic review and network meta-analysis of randomised controlled trials
Bibliographic record
Abstract
Introduction Colonoscopy quality can vary depending on endoscopist-related factors. Quality indicators, such as adenoma detection rate (ADR), have been adopted to reduce variations in care. Several interventions aim to improve ADR, but these fall into several domains that have traditionally been difficult to compare. We will conduct a systematic review and network meta-analysis of randomised controlled trials evaluating the efficacies of interventions to improve colonoscopy quality and report our findings according to clinically relevant interventional domains. Methods and analysis We will search MEDLINE (Ovid), PubMed, EMBASE, CINAHL, Web of Science, Scopus and Evidence-Based Medicine from inception to September 2022. Four reviewers will screen for eligibility and abstract data in parallel, with two accordant entries establishing agreement and with any discrepancies resolved by consensus. The primary outcome will be ADR. Two authors will independently conduct risk of bias assessments. The analyses of the network will be conducted under a Bayesian random-effects model using Markov-chain Monte-Carlo simulation, with 10 000 burn-ins and 100 000 iterations. We will calculate the ORs and corresponding 95% credible intervals of network estimates with a consistency model. We will report the impact of specific interventions within each domain against standard colonoscopy. We will perform a Bayesian random-effects pairwise meta-analysis to assess heterogeneity based on the I 2 statistic. We will assess the certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation framework for network meta-analyses. Ethics and dissemination Our study does not require research ethics approval given the lack of patient-specific data being collected. The results will be disseminated at national and international gastroenterology conferences and peer-reviewed journals. PROSPERO registration number CRD42021291814.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.103 | 0.174 |
| Meta-epidemiology (narrow) | 0.009 | 0.006 |
| Meta-epidemiology (broad) | 0.025 | 0.030 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.067 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".