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Record W4283576871 · doi:10.1177/22925503221109072

Resident Exposure and Involvement in Core Procedural Competencies within Pediatric Plastic Surgery

2022· article· en· W4283576871 on OpenAlexaffabout
Josephine A. D’Abbondanza, Jessica G. Shih, Aaron Knox, Nick Zhygan, Mitchell H. Brown, Joel Fish, Douglas J. Courtemanche

Bibliographic record

VenuePlastic Surgery · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of Toronto
Fundersnot available
KeywordsMedicinePediatric SurgeonResidency trainingDelphi methodCurriculumPlastic surgeryCore competencyMedical educationPediatric surgerySurgeryPsychologyContinuing education

Abstract

fetched live from OpenAlex

Introduction: The implementation of competency-based residency training in plastic surgery is underway. Key competencies in plastic surgery have been previously identified, however, within the domain of pediatrics, data suggest limited exposure throughout training for Canadian graduates. This study aims to identify the exposure and involvement of residents in core pediatric cases. Methods: We performed a retrospective, multicenter review of plastic surgery resident case logs (T-Res, POWER, New Innovations) across 10 Canadian, English-speaking training programs between 2004 and 2014. Case logs were coded according to the 8 core pediatric competencies previously identified by a modified Delphi technique. Results: A total of 3061 of 59 405 cases (5.2%) logged by 55 graduating residents were core pediatric procedures with an average of 55.6 ± 23.0 cases logged per resident. The top 3 most commonly logged procedures were cleft lip repair, cleft palate repair, and setback otoplasty. The number of cases per program varied widely with the most at 731 and least at 85 logged cases. Roles across procedures have wide variation and residents are most commonly identified as the assistant rather than surgeon or co-surgeon. Conclusion: These findings highlight variability both within and across residency programs with a paucity of exposure and involvement in pediatric plastic surgery cases. This may present a conflict between current recommendations for residency-specific procedural competencies and true clinical exposure. Further curriculum development and simulation may be of benefit.

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)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.247
Teacher spread0.196 · 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.

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
Published2022
Admission routes2
Has abstractyes

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