Finding Medicine’s Moneyball: How Lessons From Major League Baseball Can Advance Assessment in Precision Education
Bibliographic record
Abstract
ABSTRACT: Precision education (PE) leverages longitudinal data and analytics to tailor educational interventions to improve patient, learner, and system-level outcomes. At present, few programs in medical education can accomplish this goal as they must develop new data streams transformed by analytics to drive trainee learning and program improvement. Other professions, such as Major League Baseball (MLB), have already developed extremely sophisticated approaches to gathering large volumes of precise data points to inform assessment of individual performance.In this perspective, the authors argue that medical education-whose entry into precision assessment is fairly nascent-can look to MLB to learn the possibilities and pitfalls of precision assessment strategies. They describe 3 epochs of player assessment in MLB: observation, analytics (sabermetrics), and technology (Statcast). The longest tenured approach, observation, relies on scouting and expert opinion. Sabermetrics brought new approaches to analyzing existing data in a way that better predicted which players would help the team win. Statcast created precise, granular data about highly attributable elements of player performance while helping to account for nonplayer factors that confound assessment such as weather, ballpark dimensions, and the performance of other players. Medical education is progressing through similar epochs marked by workplace-based assessment, learning analytics, and novel measurement technologies. The authors explore how medical education can leverage intersectional concepts of MLB player and medical trainee assessment to inform present and future directions of PE.
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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 | 0.026 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".