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Record W2250952259 · doi:10.1123/ijatt.17.2.21

Musculoskeletal Injury Evaluation Standards for Different Disciplines

2012· article· en· W2250952259 on OpenAlexaffabout
Mark R. Lafave, Nicholas G. Mohtadi, Denise Chan

Bibliographic record

VenueInternational Journal of Athletic Therapy & Training · 2012
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of CalgaryMount Royal University
Fundersnot available
KeywordsObjective structured clinical examinationCompetence (human resources)MedicineMedical educationHealth carePhysical therapyAthletic trainingCompetency assessmentSports medicinePsychology

Abstract

fetched live from OpenAlex

Abstract Evaluation of musculoskeletal injuries requires special knowledge and skills that are shared by different health professions, but the process used to establish a diagnosis is not necessarily the same. Medicine has employed the objective structured clinical exams (OSCE) to assess clinical competence. The performances of two Canadian athletic therapists were assessed by two different methods for assessment of clinical competence in the evaluation of knee injuries. On the basis of existing standards, both of the athletic therapists would have passed the examination using the Standardized Orthopedic Assessment Tool currently used to assess the clinical competence of athletic therapy students, but both would have failed using the Academy of Sport and Exercise Medicine OSCE for sport medicine physicians. The failure could be because the performances of only two subjects were assessed, but it could also be because different constructs are represented by the two methods. If we truly want to provide patient-centered care, it should be important to have similar standards, regardless of the clinician’s professional discipline.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.455
Teacher spread0.395 · 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 designOther design
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

Citations2
Published2012
Admission routes2
Has abstractyes

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