Triage processes at multidisciplinary chronic pain clinics: An international review of current procedures
Bibliographic record
Abstract
Background: Multidisciplinary pain clinics are considered the gold standard for the treatment of chronic pain, yet access to such clinics is difficult and patients’ conditions deteriorate while waiting. Instituting a triage process is one way of reducing wait time for some patients and ensuring optimal access given the limited resources available. Surprisingly, there are no established guidelines on how to optimally triage chronic pain patients at tertiary multidisciplinary pain clinics.Aims: The goal of this study was to gather information regarding existing triage systems in multidisciplinary chronic pain clinics worldwide as an initial step toward establishing a definitive evidence-based set of triage guidelines.Methods: A total of 66 multidisciplinary pain clinics worldwide completed an online survey detailing current triage practices at their clinic. The survey was distributed via international and national pain associations.Results: Results showed that the vast majority of multidisciplinary pain clinics (94%) use a triage system, yet many difficulties with these systems have been identified (time requirement, administrative burden, lack of control over scheduling, missing high-priority patients, and prioritizing low-priority patients). The level of satisfaction was noted to be higher in those clinics using a structured triage template.Conclusions: This study identified a need for the elaboration of best practice clinical guidelines for triage processes at tertiary pain clinics. The use of a structured referral template could become a central element to such guidelines.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".