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 Twelve raters with 51 (18–156) months of clinical experience participated. Percent agreement ranged from 8%-100% for both cases for individual impairments. When assessing the unique combination of three impairments selected, inter-rater agreement was less than what is expected due to chance alone (κ = −0.09, p = .92; κ = −0.09, p = .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 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 | 0.047 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".