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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".