The ABMS MOC Part III Examination: Value, Concerns, and Alternative Formats
Bibliographic record
Abstract
This article describes the presentations and discussions at a conference co-convened by the Council on Medical Education of the American Medical Association (AMA) and by the American Board of Medical Specialties (ABMS). The conference focused on the ABMS Maintenance of Certification (MOC) Part III Examination. This article, reflecting the conference agenda, covers the value of and evidence supporting the examination, as well as concerns about the cost of the examination, and-given the current format-its relevance. In addition, the article outlines alternative formats for the examination that four ABMS member boards are currently developing or implementing. Lastly, the article presents contrasting views on the approach to professional self-regulation. One view operationalizes MOC as a high-stakes, pass-fail process while the other perspective holds MOC as an organized approach to support continuing professional development and improvement. The authors hope to begin a conversation among the AMA, the ABMS, and other professional stakeholders about how knowledge assessment in MOC might align with the MOC program's educational and quality improvement elements and best meet the future needs of both the public and the physician community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.083 | 0.217 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".