Improving access to chronic pain care with central referral and triage: The 6-year findings from a single-entry model
Bibliographic record
Abstract
Background: Despite the established efficacy of multidisciplinary chronic pain care, barriers such as inflated referral wait times and uncoordinated care further hinder patient health care access. Aims: Here we describe the evolution of a single-entry model (SEM) for coordinating access to chronic pain care across seven hospitals in Toronto and explore the impact on patient care 6 years after implementation. Methods: In 2017, an innovative SEM was implemented for chronic pain referrals in Toronto and surrounding areas. Referrals are received centrally, triaged by a clinical team, and assigned an appointment according to the level of urgency and the most appropriate care setting/provider. To evaluate the impact of the SEM, a retrospective analysis was undertaken to determine referral patterns, patient characteristics, and referral wait times over the past 6 years. Results: Implementation of an SEM streamlined the number of steps in the referral process and led to a standardized referral form with common inclusion and exclusion criteria across sites. Over the 6-year period, referrals increased by 93% and the number of unique providers increased by 91%. Chronic pain service wait times were reduced from 299 (±158) days to 176 (±103) days. However, certain pain diagnoses such as chronic pelvic pain and fibromyalgia far exceed the average. Conclusions: The results indicate that the SEM helped reduce wait times for pain conditions and standardized the referral pathway. Continued data capture efforts can help identify gaps in care to enable further health care refinement and improvement.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".