Chronic noncancer pain: characteristics of patients prescribed opioids by community physicians and referred to a tertiary pain clinic.
Bibliographic record
Abstract
OBJECTIVE: To describe the characteristics of patients with chronic noncancer pain (CNCP) prescribed opioids by community physicians and referred to a tertiary pain clinic. DESIGN: Cross-sectional, descriptive study. SETTING: A tertiary care, hospital-based pain clinic in Toronto, Ont. PARTICIPANTS: A total of 455 consecutive patients newly referred to the pain clinic by community physicians. MAIN OUTCOME MEASURES: Data on demographic characteristics, pain ratings, and medication intake were obtained using standardized collection forms and retrospective chart review. Patients were classified by diagnosis: group 1 patients had biomedical disorders only, group 2 patients had biomedical disorders and psychological factors, and group 3 patients had psychological factors only. Patients were also categorized based on opioid use: no opioid use (NOU); low opioid use (LOU), with a daily morphine-equivalent dosage (MED) of 200 mg or less; or high opioid use (HOU), with a daily MED of more than 200 mg. RESULTS: In the general study population, 63% of patients were taking opioids, with 1 in 5 exceeding an MED of 200 mg daily. In group 1, 59% of patients used opioids and 10% had HOU; 66% of patients in groups 2 and 3 were taking opioids, with 21% and 26% classified as having HOU. The mean (SD) daily MED for groups 2 and 3 HOU patients combined was significantly higher than that of group 1 HOU patients: 575.7 (472.9) mg/d versus 284.9 (74.6) mg/d, respectively. Men were twice as likely as women to have HOU; Canadian-born patients were 3 times as likely as foreign-born patients to have HOU. Psychoactive drugs were coprescribed in 61% of LOU patients and 76% of HOU patients. Greater opioid use was associated with group 2 and 3 diagnoses, male sex, Canadian-born origin, and high pain scores. CONCLUSION: Our results indicate that male, Canadian-born CNCP patients presenting with psychological morbidity or comorbidity and reporting higher pain severity ratings were more likely to receive opioids. Additionally, many CNCP patients referred to our tertiary care pain clinic were receiving opioids in excess of a 200-mg/d MED. More studies are needed to determine which factors lead to high-dose opioid prescribing in a subset of this CNCP population.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".