The Use of Learning Analytics to Enable Detection of Underperforming Trainees
Bibliographic record
Abstract
OBJECTIVE: This study aims to investigate at-risk scores of semiannual Accreditation Council for Graduate Medical Education (ACGME) Milestone ratings for vascular surgical trainees' final achievement of competency targets. SUMMARY BACKGROUND DATA: ACGME Milestones assessments have been collected since 2015 for Vascular Surgery. It is unclear whether milestone ratings throughout training predict achievement of recommended performance targets upon graduation. METHODS: National ACGME Milestones data were utilized for analyses. All trainees completing 2-year vascular surgery fellowships in June 2018 and 5-year integrated vascular surgery residencies in June 2019 were included. A generalized estimating equations model was used to obtain at-risk scores for each of the 31 subcompetencies by semiannual review periods, to estimate the probability of trainees achieving the recommended graduation target based on their previous ratings. RESULTS: A total of 122 vascular surgery fellows (VSFs) (95.3%) and 52 integrated vascular surgery residents (IVSRs) (100%) were included. VSFs and IVSRs did not achieve level 4.0 competency targets at a rate of 1.6% to 25.4% across subcompetencies, which was not significantly different between the 2 groups for any of the subcompetencies ( P = 0.161-0.999). Trainees were found to be at greater risk of not achieving competency targets when lower milestone ratings were assigned, and at later time-points in training. At a milestone rating of 2.5, with 1 year remaining before graduation, the at-risk score for not achieving the target level 4.0 milestone ranged from 2.9% to 77.9% for VSFs and 33.3% to 75.0% for IVSRs. CONCLUSION: The ACGME Milestones provide early diagnostic and predictive information for vascular surgery trainees' achievement of competence at completion of training.
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".