Physical Functioning and Opioid use in Patients with Neuropathic Pain
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between opioid dosage and ongoing therapy with physical function and disability in patients with neuropathic pain (NeP). DESIGN: Secondary analysis of a prospective cohort. SETTING: Multicenter clinical NeP registry. SUBJECTS: Seven hundred eighty-nine patients treated for various NeP diagnoses. METHODS: The following measures were included: dependent variables. 12-month self-reported physical function (pain disability index [PDI] and medical outcomes study short form-12 physical function [PCSS-12]); independent variables: baseline opioid dose (none, ≤200 mg and >200 mg of morphine equivalent), ongoing opioid use; potential confounding variables: age, sex, baseline pain intensity, and psychological distress (profile of mood states). Analysis of covariance models was created to examine the relationship between opioid therapy and both physical functioning outcomes with adjustment for confounding. RESULTS: Complete data was available for 535 patients (68%). Compared with the lower and high dose opioid groups, NeP patients not taking opioids had statistically lower disability and higher physical functioning scores, after adjusting for disease severity. Compared with patients prescribed opioid therapy on an ongoing basis, NeP patients who were not prescribed had statistically lower disability and higher physical functioning scores, after adjusting for disease severity. Improvements in disability and physical functioning scores from baseline and 12-months in all groups were modest and may not be clinically significant. CONCLUSIONS: Physical functioning and disability did not improve in patients with NeP who were prescribed opioids compared with those who are not prescribed, even after adjusting for disease severity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".