Consolidation, Systematic Appraisal and Comparison of Guideline Recommendations Regarding Management of Chronic Pain: Protocol for a Digital Chronic Pain Recommendation Map
Bibliographic record
Abstract
ABSTRACT Introduction Chronic pain affects 1 in 5 adults and children globally; however, management remains highly variable and low value care is common. Inconsistent recommendations among clinical practice guidelines for management of chronic pain contribute to suboptimal patient management. We will develop and disseminate a living digital chronic pain recommendation map (e‐Chronic Pain RecMap) to identify trustworthy recommendations in three high priority areas, (1) opioids, (2) cannabis for medical purposes, and (3) spine‐related interventional procedures for chronic pain. Methods The project comprises three phases. A planning phase to engage a diverse group of interest holders to co‐design a team structure with our knowledge users to ensure an efficient and effective workflow. Through a search of electronic databases and a manual search of international websites, a development phase to systematically identify relevant guidelines published in any language since 2019. In this development phase, we will update our searches every 6 months. Also, we will appraise the reporting quality of eligible guidelines using the AGREE‐II instrument, followed by appraisal of recommendation‐level quality using AGREE‐REX for guidelines meeting a defined threshold. In a next step, we will extract data from guidelines including relevant equity information using infrastructure in GRADEPro, and explore divergence and compare recommendations answering the same guideline question. Lastly, we will develop plain language summaries of trustworthy recommendations and decision aids to support patient‐physician decision making, and translate the platform to French, Spanish, and Mandarin. Finally, a mobilization phase in which, with our interest holders, we will co‐create strategies to disseminate the RecMap to relevant target users. Discussion The Chronic Pain RecMap will enhance use of trustworthy guideline recommendations by people living with chronic pain, clinicians, and decision makers. Successful uptake will optimize evidence‐based pain management.
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".