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Record W4403406310 · doi:10.1097/xcs.0000000000001227

Perspectives on Surgical Leadership: Panel Discussion at the Society for Clinical Vascular Surgery

2024· article· en· W4403406310 on OpenAlexaff
Jean Bismuth, Murray L. Shames, Audra A. Duncan, Erica L. Mitchell, David H. Howard, Kenneth M. Slaw, Perry DeAugustine, Nadia C. Wise, Jason T. Lee

Bibliographic record

VenueJournal of the American College of Surgeons · 2024
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineSalientLeadership developmentMedical educationPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Leadership is a skill that all surgeons are confronted with in some capacity. Surprisingly, in the US most training programs do not offer a structured program in leadership and there certainly are no metrics used to assess leadership competency. As a response to this, at the Society for Clinical Vascular Surgery, a panel of leaders in vascular surgery both national and international along with leadership experts discussed some of the salient issues in this space. This article is the result of this discussion and serves as a good framework for understanding needs and current shortcomings of leadership training.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.126
GPT teacher head0.369
Teacher spread0.244 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

Explore more

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