The Changing Landscape of Maintenance of Certification: History, Value and Evidence Base, and Future Impact on Forensic Pathology
Bibliographic record
Abstract
Maintenance of certification (MOC) is a current hot topic in all disciplines of medicine, and no less so in pathology and forensic pathology, specifically. The extent of physician discontent with MOC has risen to a fevered pitch over the past few years, with reporting on specialty board activities and physicians' reactions reaching the popular media. This invited review authored by several of the members of the National Association of Medical Examiners Education Subcommittee on the Development of Self-Assessment Modules provides an overview of MOC as it relates to forensic pathology. We address the history of MOC and its value as stated by the certifying bodies that created, promote, and administer MOC, including the American Board of Medical Specialties and American Board of Pathology. We further provide an analysis of the existing medical literature proposed as an evidence base for MOC, which is somewhat limited in its scope, particularly nonrobust in pathology, and nonexistent in forensic pathology. We discuss recent changes that medical specialties have made to prescribed MOC programs, potential alternatives to MOC, and the impact that MOC in its current and potential future forms may have on the field of forensic pathology, including effects on the workforce, courts of law, and training pathways.
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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 | 0.031 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".