Assessing Quality of Referrals to a Community-Based Chronic Pain Clinic
Bibliographic record
Abstract
Introduction: Because patients with chronic pain are complex, with significant medical and psychiatric comorbidities, referrals to specialty pain clinics are often necessary. The present study explores the quality of information submitted and the profile of referring physicians associated with rejected patient referrals by a community pain clinic. Methods: A retrospective cross-sectional study was conducted on a series of consecutive new patient referrals rejected by a noninterventional community pain clinic (November 2021-June 2022). Data were collected on the reasons for rejected referrals and physicians responsible for these referrals using the public database of the College of Physicians and Surgeons of Ontario. Results: During the study period, 120 new referrals made by 99 physicians (88% primary care providers, or PCPs; male : female ratio 1:1.2; 53% Canadian university graduates) were rejected because of inadequate information (62%) or because they were inappropriate (38%). Only 46% of the rejected referrals were resubmitted within a median of 7 days (range 0-96 days) and accepted. Half of the non-resubmitted referrals could have been accepted if the referring provider had sent in the missing information. Conclusion: A significant number of referrals to our pain clinic (primarily from PCPs) are rejected for mainly avoidable reasons. The process of rejected referrals and resubmissions requires 92 to 126 h of additional staff time/year. Without additional health care resources, our study highlights simple but effective improvements in the referral process that could facilitate patient care, avoid unnecessary delays, and decrease possible sources of patient complaints.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".