Pharmacotherapeutic management of chronic noncancer pain in primary care: lessons for pharmacists
Bibliographic record
Abstract
PURPOSE: Describe the pharmacotherapeutic management of primary-care patients with chronic noncancer pain, assess their satisfaction with pain treatment, and identify the determinants of their satisfaction. METHODS: A cohort study was conducted in Quebec (Canada). Patients reporting chronic noncancer pain with an average pain intensity of at least 4 on a 0-10 scale (10= worst possible pain) and having an active analgesic prescription from a primary-care physician were recruited. They completed a telephone interview and a self-administered questionnaire to document their pain, emotional well-being, satisfaction with treatment, and barriers/beliefs/attitudes about pain and its treatment. Information on pharmacotherapy was based on an administrative provincial database and pharmacies' charts. Determinants of patients' satisfaction were identified using multivariate linear regression models. RESULTS: Four hundred and eighty six patients participated. Their mean age was 58.4 years and they had had pain for a mean of 11.7 years (standard deviation, ±11.1) at an average pain intensity of 6.5 in the past week. Although 90% reported adverse gastrointestinal effects, 36.4% and 54.4% of these patients took no over-the-counter or prescribed medication for constipation or nausea, respectively. On a scale from 0-100, the mean overall satisfaction score was 64.7 (95% confidence interval [CI] =63.5-65.9). Patient satisfaction was low, particularly regarding the "information about pain and its treatment" (mean 50.6; 95% CI =47.6-53.7) and "treatment efficacy" (mean 53.6; 95% CI =51.5-55.6) subscales. The overall treatment satisfaction score decreased with more pain disability, probable depression and anxiety, more barriers to pain treatment, higher incidence of nausea, and use of over-the-counter analgesics. CONCLUSION: In primary care, patients' level of satisfaction with their pain treatment is not optimal. This study underlines how the expanded scope of practice of community pharmacists may allow them to play a pivotal role in providing information, discussing barriers to pain treatment, and monitoring pain disability, and by appropriately managing pharmacotherapy to optimize effectiveness while minimizing adverse effects.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".