Using an osteoarthritis-specific pain measure in elders with cognitive impairment: a pilot study
Bibliographic record
Abstract
AIM AND BACKGROUND: The Western Ontario and McMaster Osteoarthritis Index pain scale has not been used with cognitively impaired elders to measure their knee or hip pain. This study therefore examined the usefulness of the Western Ontario and McMaster Osteoarthritis Index pain scale for measuring pain in this population. METHOD: Fourteen cognitively impaired elders with osteoarthritis of the knee or hip participated in the study. Elders' pain was assessed with the Western Ontario and McMaster Osteoarthritis Index pain scale, the Verbal Descriptor Scale and the Present Pain Intensity Scale, twice 5 minutes apart. Proxy report of pain was provided by the nursing staff. RESULTS: The correlations among the Western Ontario and McMaster Osteoarthritis Index pain scale, Verbal Descriptor Scale and Present Pain Intensity were between 0.77 and 1.00 (P < 0.01). All pain measures showed high test-retest reliability (r = 0.98-1.00, P < 0.01). Staff's pain ratings using the Western Ontario and McMaster Osteoarthritis Index pain scale were strongly correlated with elders' pain ratings using any pain measure (r = 0.68-0.80, P < 0.05 or P < 0.01). However, staff's pain ratings using either the Verbal Descriptor Scale or Present Pain Intensity had no association with elders' pain ratings (r = -0.07 to -0.30, P = NS). CONCLUSION: The study confirmed that the Western Ontario and McMaster Osteoarthritis Index pain scale is as valid and reliable as the Verbal Descriptor Scale and Present Pain Intensity for elders with moderate and mild cognitive impairment. When used by staff, the Western Ontario and McMaster Osteoarthritis Index pain scale provides a better pain assessment for cognitively impaired elders than the Verbal Descriptor Scale or Present Pain Intensity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".