A Triage Approach to Managing a Two Year Wait‐List in a Chronic Pain Program
Bibliographic record
Abstract
OBJECTIVE: Individuals with chronic pain referred to specialist chronic pain management programs frequently wait months to years for assessment and care. In the authors' pain management program, approximately 600 patients are on the waiting list. An innovative recommendation program to encourage and educate referring physicians to continue active care of pain during this waiting period was developed. METHODS: All referrals to the Queen Elizabeth II Health Sciences Centre's Pain Management Unit for a one-year period were reviewed and triaged as either 'regular waiting list' or 'fast track'. Patients in the fast track group were seen within four months and required limited interventions or were urgent in nature. The regular waiting list group waited up to 27 months for assessment and development of a treatment plan. Treatment recommendations were faxed to the referring physician. A follow-up questionnaire was sent to each physician to assess whether these treatment recommendations were useful. RESULTS: Recommendations were faxed for 297 patients. One hundred forty-nine physicians returned the follow-up questionnaire. Ninety-five physicians used the recommendations and 68 patients followed the recommendations. Seventy-nine physicians felt that the recommendations were helpful to them in their care of the patient. For 39 patients, the recommendations were helpful. The most frequently used recommendations were those on medications (eg, tricylic antidepressants, anticonvulsants, nonsteroidal anti-inflammatory drugs and controlled-release opioids). Other modalities included participation in an interdisciplinary group program and physiotherapy. CONCLUSIONS: A triage review process with recommendations faxed to referring physicians was developed and put into action for one year. The recommendations were used by 32% of the physicians (64% of responding physicians). Fifty-three per cent of responding physicians felt that the recommendations were helpful in the care of their patient. This process led to a benefit in care, as perceived by the physician, in 26% of patients (of physicians who returned the questionnaire [13% of all patients]) on the waiting list for a tertiary care pain management unit.
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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 | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".