The association between knee temperature and pain in elders with osteoarthritis of the knee: a pilot study
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis is a highly prevalent, disabling condition that causes significant pain and suffering among older adults. Cognitively impaired elders are as susceptible to osteoarthritis as their peers. However, since they have diminished ability to communicate their pain, an alternative method to detect osteoarthritis pain in cognitively impaired elders is urgently needed. Because the late stages of osteoarthritis involve joint inflammation with a mild increase in local temperature, skin surface temperature might reasonably be expected to serve as a proxy measure of osteoarthritis pain. If knee surface temperature could be shown to predict pain in cognitively intact elders, it could be used as a proxy measure of pain for cognitively impaired elders. AIM: To test this, the study reported here assessed the relationship between knee surface temperature and pain in cognitively intact elders with osteoarthritis of the knee. METHODS: We recruited 12 cognitively intact elders with documented osteoarthritis of the knee who lived in retirement apartments. Elders' pain and knee temperature were measured three times on three separate occasions. Osteoarthritis pain of the knee was measured using the Knee Pain Scale and the Western Ontario and McMaster Osteoarthritis Index pain subscale. A YSI Model 4000 Dual Channel Display Telethermometer was used to measure knee temperature. RESULTS: We found no significant associations between knee temperature and any of the pain measures used, with one exception. However, body mass index, amount of pain medication used and activity level observed during the interview were significantly related to elders' pain. CONCLUSION: Knee temperature does not appear to predict knee pain in elders with osteoarthritis of the knee. Body mass index, use of pain medication and activity level are better predictors of this.
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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 | 0.000 |
| 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) | 0.000 | 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".