Operationalization of the new Pain and Disability Drivers Management model: A modified Delphi survey of multidisciplinary pain management experts
Bibliographic record
Abstract
BACKGROUND: We recently proposed the Pain and Disability Drivers Management (PDDM) model, which was designed to outline comprehensive factors driving pain and disability in low back pain (LBP). Although we have hypothesized and proposed 41 elements, which make up the model's five domains, we have yet to assess the external validity of the PDDM's elements by expert consensus. RESEARCH OBJECTIVES: This study aimed to reach consensus among experts regarding the different elements that should be included in each domain of the PDDM model. RELEVANCE: The PDDM may assist clinicians and researchers in the delivery of targeted care and ultimately enhance treatment outcomes in LBP. METHODS: Using a modified Delphi survey, a two-round online questionnaire was administered to a group of experts in musculoskeletal pain management. Participants were asked to rate the relevance of each element proposed within the model. Participants were also invited to add and rate new elements. Consensus was defined by a greater than or equal to 75% level of agreement. RESULTS: A total of 47 (round 1) and 33 (round 2) participants completed the survey. Following the first round, 38 of 41 of the former model elements reached consensus, and 10 new elements were proposed and later rated in the second round. Following this second round, consensus was reached for all elements (10 new + 3 from first round), generating a final model composed of 51 elements. CONCLUSION: This expert consensus-derived list of clinical elements related to the management of LBP represents a first step in the validation of the PDDM model.
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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 | 0.082 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".