Construct Validity Evidence for ACGME Milestones in Surgical Specialties: A Systematic Review
Bibliographic record
Abstract
Background The Accreditation Council for Graduate Medical Education (ACGME) Milestones use has been formative and low-stakes to date, and transitioning to higher-stakes applications in a truly competency-based medical education (CBME) system requires extensive validity evidence. Surgical specialties, with their unique demands for procedural skills and operative experience, represent a critical context for evaluating the validity of Milestones. Objective To synthesize studies reporting validity evidence for the ACGME Milestones in surgical specialties. Methods This systematic review was conducted based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search was conducted across 8 databases and references to identify studies that reported validity evidence for Milestones in surgical specialties. Literature was reviewed for inclusion using Covidence and coded based on Messick’s framework. The quality of the studies was evaluated using the Medical Education Research Study Quality Instrument. Results A total of 114 studies were included from 2013 to 2023. The primary source of validity evidence (n=45, 39.5%) was relations to other variables (knowledge and skills, learner characteristics, patient/health care, social-emotional variables), followed by response processes (n=38, 33.3%: interrater reliability, rating processes, structure of Clinical Competency Committee, rater training, longitudinal reliability, straightlining) and consequences (n=29, 25.4%: value and utility, intended use, anticipated impact). Only 12 studies (10.5%) reported internal structure evidence. Conclusions This study provides insights into understanding what constitutes validity evidence within the context of ACGME Milestones in surgical specialties. This review highlights areas where further research is needed to support the moderate to high-stakes use of Milestones in a CBME system.
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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 | 0.009 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".