Optimizing adenoma detection in screening-related colonoscopy
Bibliographic record
Abstract
INTRODUCTION: Screening-related colonoscopy is a vital component of screening initiatives to both diagnose and prevent colorectal cancer (CRC), with prevention being reliant upon early and accurate detection of pre-malignant lesions. Several strategies, techniques, and interventions exist to optimize endoscopists' adenoma detection rates (ADR). AREAS COVERED: This narrative review provides an overview of the importance of ADR and other colonoscopy quality indicators. It then summarizes the available evidence regarding the effectiveness of the following domains in terms of improving ADR: endoscopist factors, pre-procedural parameters, peri-procedural parameters, intra-procedural strategies and techniques, antispasmodics, distal attachment devices, enhanced colonoscopy technologies, enhanced optics, and artificial intelligence. These summaries are based on an electronic search of the databases Embase, PubMed, and Cochrane performed on 12 December 2022. EXPERT OPINION: Given the prevalence and associated morbidity and mortality of CRC, the quality of screening-related colonoscopy quality is appropriately prioritized by patients, endoscopists, units, and payers alike. Endoscopists performing colonoscopy should be up to date regarding available strategies, techniques, and interventions to optimize their performance.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".