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VALIDATING THE 3-STEP RETURN TO PLAY DECISION MAKING MODEL

2014· article· en· W2090283824 on OpenAlexaff
Ian Shrier, G. O. Matheson, Mathieu Boudier‐Revéret, Russell Steele

Bibliographic record

VenueBritish Journal of Sports Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsVignetteContext (archaeology)MedicineAthletesPhysical therapyInjury preventionPoison controlPhysical medicine and rehabilitationPsychologyEmergency medicineSocial psychology

Abstract

fetched live from OpenAlex

Background A recent return to play decision-making (RTP-DM) model for sport medicine has organized the underlying concepts into 3 steps but has not yet been validated. Objective To examine the validity of the 3-step RTP-DM model recently proposed. Design Repeated measures cross-over survey design. Setting World-wide. Participants American College of Sports Medicine clinicians involved in RTP-DM. Risk factor assessment We provided clinical vignettes of injuries and illnesses in athletes to participants through an online survey. Each vignette included examples of 3 factor types: increasing injury severity, changing risk associated with sport (e.g. different positions), and changing non-injury risk factors (e.g. financial considerations). Main Outcome Measurements For each vignette, participants indicated the level of activity restriction they would recommend (6 options from No Restrictions to No Activity) in accordance with the risk they placed on continued participation. We analyzed the data using multiple regression, adjusting for the correlated participant outcomes, to measure how changes in factors affected individual participants. Results The estimated participation rate for those involved in RTP decisions was 24.7%. As expected, we found that clinicians increase restrictions as injury severity increases. We also found that changing factors related to sport risk, and changing factors that are unrelated to sport risk will affect RTP decisions, although the effect is context-dependent and does not occur equally across all injury severities and clinical cases. The effect was also observed in each subgroup examined that included sex, age, specialty, region of training, academic status, and years of experience making RTP decisions. Conclusions Our findings that clinicians from a wide variety of backgrounds will change RTP recommendations based on clinical vignettes with changing injury severity, sport risk modifiers and decision modifiers provides evidentiary support for the 3-step model for RTP decision making recently proposed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.072
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.015
GPT teacher head0.302
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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
Published2014
Admission routes1
Has abstractyes

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