Pain management and pain characteristics in obese and normal weight patients before joint replacement
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: The objective was to compare the extent of pain interference and pain medication among persons who were classified as obese [body mass index (BMI)≥ 30 kg m(-2) ] and normal weighted (BMI ≤ 25 kg m(-2) ), before a hip or knee replacement surgery. METHODS: Patients candidate for an orthopaedic surgery were successively enrolled, over a 6-month period, and classified in either the normal weight (BMI ≤ 25 kg m(-2) ) or the obese (BMI ≥ 30 kg m(-2) ) categories. Data were collected using self-administered questionnaires with items concerning pain characteristics, pain medication and pain interference. Two standardized questionnaires were associated: the Brief Pain Inventory (BPI) and the Hospital Anxiety and Depression scale (HAD). RESULTS: Fifty-two obese patients (candidates for 24 hip replacements and 28 knee replacements) and 51 non-obese (23 hip replacements and 28 knee replacements) were enrolled. Obese patients suffered from a higher rate of acute pain episodes than non-obese patients (65 versus 44%, P<0.05). Pain interference on walking distance, sleep and relations with others was higher in obese patients. HAD score showed no significant difference between groups. The use of strong opioids and of non-steroidal anti-inflammatory drugs (NSAIDs) was significantly more important in obese patients (13 versus 0% and 31 versus 14%). CONCLUSIONS: Obese patients suffer more significantly of unrelieved chronic pain, which lowers considerably their quality of life. Pain relief is more difficult to obtain, as it requires stronger pain medication and NSAIDs.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".