Accuracy of self-assessment in gastrointestinal endoscopy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Assessment is necessary to ensure both attainment and maintenance of competency in gastrointestinal (GI) endoscopy, and this can be accomplished through self-assessment. We conducted a systematic review with meta-analysis to evaluate the accuracy of self-assessment among GI endoscopists. METHODS: This was an individual participant data meta-analysis of studies that investigated self-assessment of endoscopic competency. We performed a systematic search of the following databases: Ovid MEDLINE, Ovid EMBASE, Wiley Cochrane CENTRAL, and ProQuest Education Resources Information Center. We included studies if they were primary investigations of self-assessment accuracy in GI endoscopy that used statistical analyses to determine accuracy. We conducted a meta-analysis of studies using a limits of agreement (LoA) approach to meta-analysis of Bland-Altman studies. RESULTS: After removing duplicate entries, we screened 7138 records. After full-text review, we included 16 studies for qualitative analysis and three for meta-analysis. In the meta-analysis, we found that the LoA were wide (-41.0 % to 34.0 %) and beyond the clinically acceptable difference. Subgroup analyses found that both novice and intermediate endoscopists had wide LoA (-45.0 % to 35.1 % and -54.7 % to 46.5 %, respectively) and expert endoscopists had narrow LoA (-14.2 % to 21.4 %). CONCLUSIONS: GI endoscopists are inaccurate in self-assessment of their endoscopic competency. Subgroup analyses demonstrated that novice and intermediate endoscopists were inaccurate, while expert endoscopists have accurate self-assessment. While we advise against the sole use of self-assessment among novice and intermediate endoscopists, expert endoscopists may wish to integrate it into their 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 | 0.068 | 0.154 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.047 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".