Physical therapy students’ application of an imaging decision rule for acute knee pain
Bibliographic record
Abstract
Background Evidence supports direct referral for imaging by physical therapists. Accuracy and self-efficacy for imaging decisions have not been investigated in entry-level doctor of physical therapy (DPT) students.Objective The purpose of this study was to understand the relationship between entry-level DPT instruction and accuracy and self-efficacy for imaging referral due to acute knee trauma. A second purpose was to identify relationships between accuracy and self-efficacy.Methods An online survey was sent via e-mail to program directors in accredited DPT programs in the United States with an invitation to forward the survey to DPT students. The survey captured demographic information and included five questions that assessed the respondent’s ability to apply the Ottawa Knee Rules (OKR). Self-efficacy was assessed using the Physiotherapist Student Self-Efficacy (PSE) questionnaire, a self-rated 5-point Likert scaled tool.Results Of 240 surveys, DPT students who completed imaging coursework had greater accuracy and higher self-efficacy (68.0% correct (95% CI, 63.6–72.5), PSE = 3.67, P < .001) compared to students who had not (45.8% correct (95% CI, 40.8–50.7), PSE = 2.67, P < .001). Conclusion: Accuracy by DPT students who completed imaging coursework was significantly improved and comparable to values from autonomous providers.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".