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Record W3200099890 · doi:10.1177/24730114211033299

Trends in Leadership Within Orthopedic Foot and Ankle Fellowships

2021· article· en· W3200099890 on OpenAlexaboutno aff
Joshua P. Weissman, Cody Goedderz, Muhammad Mutawakkil, Peter R. Swiatek, Erik B. Gerlach, Milap Patel, Anish R. Kadakia

Bibliographic record

VenueFoot & Ankle Orthopaedics · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsOrthopedic surgeryMedicineAnkleFoot (prosody)Ethnic groupPhysical therapyScopusFoot and ankle surgerySurgeryMEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: No study in the orthopedic literature has analyzed the demographic characteristics or surgical training of foot and ankle fellowship directors (FDs). Our group sought to illustrate demographic trends among foot and ankle fellowship leaders. METHODS: The American Orthopaedic Foot & Ankle Society (AOFAS) Fellowship Directory for the 2021 to 2022 program year was queried in order to identify all foot and ankle fellowship leaders at programs currently offering positions in the United States and Canada. Data points gathered included age, sex, race/ethnicity, location of surgical training, time since training completion until FD appointment, length in FD role, and individual research H-index. RESULTS: We identified 68 fellowship leaders, which consisted of 48 FDs and 19 co-FDs. Sixty-five individuals (95.6%) were male, and 3 (4.4%) were female. As regards race/ethnicity, 88.2% of the leadership was Caucasian (n = 60), 7.4% was Asian American (n = 5), 1.5% was Hispanic/Latino (n = 1), and 1.5% was African American (n = 1). The average age was 51.5 years, and the calculated mean Scopus H-index was 15.28. The mean duration from fellowship training to fellowship leader position was 11.23 years. CONCLUSION: Leaders within foot and ankle orthopedic surgery are characterized by research prowess and experience, but demographic diversity is lacking. LEVEL OF EVIDENCE: Level III.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.085
GPT teacher head0.298
Teacher spread0.213 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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