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Record W4411965698 · doi:10.1016/j.jposna.2025.100227

Handling Risky Situations, Especially the Unexpected, With Elite Performance

2025· article· en· W4411965698 on OpenAlexaff
Peter M. Waters, Kevin J. Farrell, Donald S. Bae, Rebecca E. Glavin, Steven L. Frick

Bibliographic record

VenueJournal of the Pediatric Orthopaedic Society of North America · 2025
Typearticle
Languageen
FieldPsychology
TopicLeadership, Courage, and Heroism Studies
Canadian institutionsGreenfield Research (Canada)
Fundersnot available
KeywordsElitePsychologyComputer securityForensic engineeringComputer scienceRisk analysis (engineering)BusinessEngineeringPolitical scienceLaw

Abstract

fetched live from OpenAlex

Surgery is stressful and carries real risk for patients, surgeons, operating room professionals, and healthcare organization. To achieve the safest and best possible outcomes, surgeons need to execute a surgical plan based on evidence-based information with the highest level of technical skill. In addition, surgeons and their teams need to "expect the unexpected," manage their emotions and behaviors productively, stay calm and focused under pressure, and enact anticipated and in-the-moment contingency plans to avoid complications.

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)0.000
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 score0.055
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.258
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 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
Published2025
Admission routes1
Has abstractyes

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