Rehabilitation Management of Neck Pain—Development of a Diagnostic Framework Based on the Pain and Disability Drivers Management Model
Bibliographic record
Abstract
RATIONALE: Neck pain is a major cause of disability worldwide, and current rehabilitation strategies show limited effectiveness. Subgrouping patients by their primary pain and disability drivers can help tailor treatments. At this end, the Pain and Disability Drivers Management (PDDM) was developed and has demonstrated preliminary effectiveness in the management of low back pain. Nevertheless, the PDDM model was only validated for this population. Adapting this framework to patients with neck pain would provide a more global view of the patient's experience of pain and support a genuine biopsychosocial intervention. AIMS AND OBJECTIVES: The aim of this study was to develop and validate the content of the PDDM model for patients living with neck pain. METHODS: Through a modified DELPHI study design, participants with clinical and research expertize in rehabilitation of neck pain were invited to participate. A questionnaire was developed using literature reviews and endorsed by a steering committee. The relevance of each element of the newly adapted model was evaluated on a 4-point Likert scale. An item reached consensus if it obtained the predefined threshold of > 78% "relevant" and "very relevant." Participants left comments on terminology and recommended items to add in early rounds. Quantitative and qualitative analyses were performed. RESULTS: An invitation was sent to 1650 potential participants, from which 155 accessed the survey, 64 completed the first round and 55 the second round. A total of 70 elements met consensus and were distributed across six domains: "Nociceptive pain drivers", "nociplastic pain drivers," "drivers associated with neuropathic pain", "comorbidity drivers", "cognitive-emotional drivers" and "environmental or lifestyle drivers, and social determinants of health." CONCLUSION: Through a modified DELPHI study, the PDDM model was updated and adapted to people with neck pain. Subsequent steps include clinical integration and measures of efficacy when used for assessment/treatment.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".