Initial Reliability of The Standardized Orthopedic Assessment Tool (SOAT)
Bibliographic record
Abstract
CONTEXT: Orthopaedic assessment skills are critical to the success of athletic therapists and trainers. The Standardized Orthopedic Assessment Tool (SOAT) has been content validated. OBJECTIVE: To establish interrater reliability of the SOAT. PATIENTS OR OTHER PARTICIPANTS: Thirty-two college students, 10 raters, and 2 standardized patients (SPs) from Calgary, Alberta, Canada. DESIGN: Randomized observational study. INTERVENTION(S): Students were allowed 30 minutes to complete a mock orthopaedic assessment of an SP with an injury specific to a region of the body (shoulder, knee, or ankle). Using the region-specific SOAT, raters and SPs evaluated students' orthopaedic assessment skills. MAIN OUTCOME MEASURE(S): The sum totals of the SOAT for 2 raters and 1 SP were used to calculate each student's performance scores for respective scenarios. Scale reliability analysis (Cronbach alpha) was completed on the SOAT for each of the 3 body-region examinations. RESULTS: The mean overall reliability of 3 SOATs (ie, ankle, knee, and shoulder) was positive: alpha = .85 with the SP scores factored into the equation and alpha = .86 without the SP scores factored into the equation. Reliability for the ankle region was highest (alpha = .91), followed by the knee (alpha = .83) and the shoulder (alpha = .82). CONCLUSIONS: The study sample size was small, but the results will enable further study with generalization to a broader audience of athletic therapists and athletic trainers. Because a baseline measure of reliability was established using a robust statistical analysis, future researchers can employ more stringent statistical analysis and focus on the effects of various pedagogical techniques to teach and learn the underlying construct of clinical competence in orthopaedic assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".