A preliminary analysis of physical therapist agreement regarding the perceived impairments in cases of runners with knee pain
Bibliographic record
Abstract
BACKGROUND: There is a scarcity of evidence describing how physical therapists use data from clinical examinations to inform the treatment of runners with knee pain. OBJECTIVE: Our purpose was to examine the between physical therapist agreement on the selection of perceived impairments in runners with knee pain. METHODS: Twelve physical therapists reviewed two cases of runners with knee pain. The cases included clinical subjective information, objective data, and review of videos of each participant running. Each rater selected up to three perceived impairments (from a list of eight) that each physical therapist would address at the next physical therapy session. Percent agreement was calculated to determine the between rater agreement on each individual perceived impairment selection and Fleiss Kappa was calculated for each unique combination of three perceived impairments per case. RESULTS: = .98) for both cases. CONCLUSION: The 12 physical therapists demonstrated poor to excellent levels of agreement when selecting an individual perceived impairment. Agreement was worse than chance when selecting a combination of three unique impairments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".