Fellowship Training in Pediatric Pathology: A Guide for Program Directors
Bibliographic record
Abstract
ABSTRACT The Accreditation Council for Graduate Medical Education (ACGME) has provided guidance for specialty and subspecialty fellowship training programs by defining 6 core competencies that must be met. Furthermore, the ACGME has defined several program requirements for pathology training, including those applicable to several pathology subspecialties. However, the requirements are broad and lack specific details, particularly as they pertain to the unique nature of pediatric pathology. The Fellowship Committee of the Society for Pediatric Pathology examined the ACGME requirements and interpreted the guidelines with respect to their application to training in pediatric pathology. The Committee worked within the ACGME guidelines to provide an expanded and more comprehensive set of guidelines for use by pediatric pathology fellowship directors and trainees. The resultant document lists the educational goals, core competencies, and program requirements with specific application to pediatric pathology. In addition, methods for assessing and documenting the progress of the individual trainees as they progress through each requirement are provided. It is to be emphasized that many of the guidelines set forthwith are flexible, and allowances should be made for individual differences of each training program.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.021 |
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".