Triage processes at multidisciplinary chronic pain clinics: An international review of current procedures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.020 |
| 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 it