A self-administered pain severity scale for patellofemoral pain syndrome
Bibliographic record
Abstract
OBJECTIVE: To develop a scale for estimating the severity of patellofemoral pain syndrome (PFPS) and to determine its reliability and validity. DESIGN: The PFPS Severity Scale (PSS) was developed following a literature search, input from clinicians, and pilot testing in people with PFPS. The final version of the instrument encompasses 10 statements regarding PFPS pain in a visual analogue format. Reliability and validity of the new scale were determined in a PFPS population. SETTING: All testing was performed at the Canadian Forces Base Kingston, Physiotherapy Department. SUBJECTS: Twenty-nine military subjects (7 female) between the ages of 20 and 48 (32 years +/- 8.9) with subjective and objective findings consistent with PFPS were recruited. Twenty-four of the participants (6 female, 31.8 years +/- 9.4) participated in the reliability phase of the study. METHODS: Reliability of the PSS was determined by comparing the scores obtained on two test days (24 hours apart). Convergent validity of the PSS was determined by comparing data from the PSS with two established knee scales: the WOMAC (Western Ontario and McMaster Universities) Osteoarthritis Index and the Hughston Foundation subjective knee scale. RESULTS: Test-retest reliability was excellent (Spearman's rho = 0.95, p < 0.0001). The correlations between the PSS and the WOMAC and Hughston scales were strong (rho = 0.72 and 0.83, p < 0.001 respectively). CONCLUSIONS: The PSS is reliable and has demonstrated convergent validity making it a useful tool for monitoring rehabilitative or surgical outcomes in clients with PFPS.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".