Detection of Residents With Progress Issues Using a Keyword–Specific Algorithm
Bibliographic record
Abstract
BACKGROUND: The literature suggests that specific keywords included in summative rotation assessments might be an early indicator of abnormal progress or failure. OBJECTIVE: This study aims to determine the possible relationship between specific keywords on in-training evaluation reports (ITERs) and subsequent abnormal progress or failure. The goal is to create a functional algorithm to identify residents at risk of failure. METHODS: A database of all ITERs from all residents training in accredited programs at Université Laval between 2001 and 2013 was created. An instructional designer reviewed all ITERs and proposed terms associated with reinforcing and underperformance feedback. An algorithm based on these keywords was constructed by recursive partitioning using classification and regression tree methods. The developed algorithm was tuned to achieve 100% sensitivity while maximizing specificity. RESULTS: There were 41 618 ITERs for 3292 registered residents. Residents with failure to progress were detected for family medicine (6%, 67 of 1129) and 36 other specialties (4%, 78 of 2163), while the positive predictive values were 23.3% and 23.4%, respectively. The low positive predictive value may be a reflection of residents improving their performance after receiving feedback or a reluctance by supervisors to ascribe a "fail" or "in difficulty" score on the ITERs. CONCLUSIONS: Classification and regression trees may be helpful to identify pertinent keywords and create an algorithm, which may be implemented in an electronic assessment system to detect future residents at risk of poor performance.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".