Referring patients with chronic noncancer pain to pain clinics
Bibliographic record
Abstract
Objective To examine the factors associated with FPs’ referrals of patients with chronic noncancer pain to a tertiary care pain clinic. Design A questionnaire-based survey; data were analyzed using univariate methods. Setting A tertiary care pain clinic in Toronto, Ont. Participants All FPs who referred patients to the clinic between 2002 and 2005. Main outcome measures Variables explored included FPs’ sex, age, and ethnic background, ethnicity of patient groups seen, and FPs’ rationale or barriers influencing referrals to specialized pain clinics. Results The response rate was 32% (47 of 148 FPs). There were no statistically significant differences between respondents and non-respondents in sex, age, duration of practice, and university of graduation, or between the variables of interest and the referral patterns of those who did respond. The mean age of respondents was 50 years; 47% of the FPs identified themselves as Canadian; and one-third of the respondents indicated that they referred more than 30 patients to pain clinics each year. The 3 most frequently cited reasons prompting referral to pain clinics were requests for nerve blocks or other injections, desire for the expertise of the program, and concerns about opioids; the 3 most prevalent barriers were long waiting lists, patient preference for other treatments, and distance from the clinic. Conclusion Although the results of our survey of FPs identify certain barriers to and reasons for referring patients to pain clinics, the results cannot be generalized owing to the small sample of FPs in our study. Larger studies of randomly selected FPs, who might or might not refer patients to pain clinics, are needed to provide a better understanding of chronic noncancer pain management needs at the primary care level.
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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".