Measuring the observer (Hawthorne) effect on adenoma detection rates
Bibliographic record
Abstract
Abstract Background and study aims An independent observer can improve procedural quality. We evaluated the impact of the observer (Hawthorne effect) on important quality metrics during colonoscopies. Patients and Methods In a single-center comparative study, consecutive patients undergoing routine screening or diagnostic colonoscopy were prospectively enrolled. In the index group, all procedural steps and quality metrics were observed and documented, and the procedure was video recorded by an independent research assistant. In the reference group, colonoscopies were performed without independent observation. Colonoscopy quality metrics such as polyp, adenoma, serrated lesions, and advanced adenoma detection rates (PDR, ADR, SLDR, AADR) were compared. The probabilities of increased quality metrics were evaluated through regression analyses weighted by the inversed probability of observation during the procedure. Results We included 327 index individuals and 360 referents in the final analyses. The index group had significantly higher PDRs (62.4% vs. 53.1%, P=0.02) and ADRs (39.4% vs. 28.3%, P=0.002) compared with the reference group. The SLDR and AADR were not significantly increased. After adjusting for potential confounders, the ADR and SLDR were 50% (relative risk [RR] 1.51; 95%, CI 1.05–2.17) and more than twofold (RR 2.17; 95%, CI 1.05–4.47) more likely to be higher in the index group than in the reference group. Conclusions The presence of an independent observer documenting colonoscopy quality metrics and video recording the colonoscopy resulted in a significant increase in ADR and other quality metrics. The Hawthorne effect should be considered an alternative strategy to advanced devices to improve colonoscopy quality in practice.
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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 | 0.049 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".