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Variability in the Duration of Designated Pediatric Orthopaedic Rotations Among US Residency Programs

2021· article· en· W3125747212 on OpenAlexaff
Bensen Fan, Caixia Zhao, Sanjeev Sabharwal

Bibliographic record

VenueJAAOS Global Research and Reviews · 2021
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsObject Research Systems (Canada)
Fundersnot available
KeywordsMedicineSpecialtyOrthopedic surgeryDuration (music)Residency trainingFamily medicineDemographicsPhysical therapyMedical educationSurgeryContinuing educationDemography

Abstract

fetched live from OpenAlex

OBJECTIVE: Our goal was to assess the variability in the assigned duration of pediatric orthopaedic rotation among US allopathic orthopaedic residency programs to see how pediatrics is incorporated into surgical education. METHODS: Using publicly available information for US allopathic orthopaedic residency programs in 2019, we retrospectively collected data on the assigned duration of pediatric orthopaedic rotation and variables such as number and sex of residents, number of orthopaedic faculty, university- versus community-based programs, outsourcing residents to unaffiliated hospital for pediatric exposure, specialty of program leadership, and presence of pediatric orthopaedic fellowship in the home program. RESULTS: One hundred thirty-eight of the 146 (95%) eligible allopathic orthopaedic residency programs provided sufficient information. The average time assigned to a pediatric rotation during residency was 6 months (range: 2 to 11 months). Overall, 43/146 (29%) programs outsourced their pediatric training to another institution. A correlation was noted between the length of pediatric rotation and percentage of pediatric orthopaedic faculty (P = 0.0007, r = 0.3). CONCLUSIONS: The impact of the variability in the duration of duration of pediatric orthopaedic rotation on the clinical knowledge and skills acquired by the resident during training needs further study.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.060
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.074
GPT teacher head0.403
Teacher spread0.329 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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