Quantifying the Pain Experience in Hip and Knee Osteoarthritis
Bibliographic record
Abstract
PURPOSE: The present study investigated whether the conceptualization of hip and knee osteoarthritis pain implicit in the Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Medical Outcomes Study Short-Form 36 (SF-36) scales is complete, or whether the addition of another scale, such as the Short-Form McGill Pain Questionnaire (MPQ-SF), provides a more complete characterization. Furthermore, the impact that mental health symptoms and catastrophizing had on these scales was investigated. METHODS: Before hip and knee arthroplasty, 200 patients completed surveys of demographic data, the WOMAC pain scale, the MPQ-SF, the SF-36 Bodily Pain scale, the Pain Catastrophizing Scale and the Hospital Anxiety and Depression Scale. Correlations between scales were calculated and linear regression modelling was used to determine the impact of mental health and catastrophizing on these three pain measures. RESULTS: A strong correlation between the WOMAC and SF-36 pain scales (r=-0.70) was found; however, both correlated only moderately with the MPQ-SF (r=0.36 and r=-0.36, respectively). Linear regression modelling showed that the Pain Catastrophizing Scale significantly predicted a greater score on all three pain scales (P<0.05). CONCLUSIONS: The addition of the MPQ-SF appears to add to a more complete quantification of the pain experience in hip and knee osteoarthritis.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".