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Record W2139869965 · doi:10.2350/12-12-1286-misc.1

Fellowship Training in Pediatric Pathology: A Guide for Program Directors

2013· article· en· W2139869965 on OpenAlexaff
Raja Rabah, Gino R. Somers, Jessica Comstock, John J. Buchino, Charles F. Timmons

Bibliographic record

VenuePediatric and Developmental Pathology · 2013
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsSubspecialtyAccreditationGraduate medical educationSpecialtyMedical educationMedicineMedical physicsPathology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.316
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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