P.117 Quantitative evaluation in competency-based medical education – a nationwide survey of the spinal surgical training landscape
Bibliographic record
Abstract
Background: The competency-based medical education (CBME) model has been recently implemented in spinal neurosurgical and orthopedic residencies. This model is grounded on entrustable professional activities (EPAs) that allow the assessment of clinical milestones. Integrating quantitative metrics to evaluate procedural competencies could refine the assessment’s scope. This survey, administered to program directors (PDs), aims to evaluate the current state and anticipated needs for quantitative evaluation to develop innovative assessment techniques. Methods: We surveyed 32 PDs of neurosurgical (N=14) and orthopedics (N=16) programs with a spine service via RedCap. We collected information on the programs’ characteristics. We inquired about existing assessment methods and the perceived values of developing quantitative metrics to assess spinal technical competencies using thirteen questions. Results: The response rate was 53%. All programs use EPAs to assess procedural competencies through direct observation in the operation room. One surgical program employed quantitative metrics for examination. Four PD valued the profitability of quantitative evaluation methods in a clinical or simulatory context. Conclusions: The use of quantitative metrics to assess spinal surgical competencies in Canadian neurosurgical and orthopedic residency programs is seldom. Despite its underutilization, PDs acknowledge the potential for quantitation to improve the accuracy and reliability of CBME assessments in both simulated and clinical settings.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".