Biopsychosocial phenotyping of patients with chronic low back pain using the pain and disability drivers management model: A retrospective cohort study
Bibliographic record
Abstract
BackgroundChronic low back pain (CLBP) is a major burden. The Pain and Disability Drivers Management (PDDM) model is a framework developed to analyse factors contributing to disability and pain in CLBP patients.ObjectiveThe primary objective was to explore the prognostic value of the PDDM model using real-life data. The secondary objective was to explore its analytical value.MethodsA monocentric retrospective cohort study included CLBP patients who underwent a multidisciplinary rehabilitation program between January 2014 and December 2020. Regression analyses were performed using the five domains of the PDDM as explanatory variables. To assess its prognostic value, the main outcome was the change in disability over the course of the program. Secondary outcomes were change in pain and return to work. To assess its analytical value, the outcome was baseline disability.ResultsCognitive-emotional domain of the PDDM predicted change in disability. Nociceptive, Nervous System Dysfunction and Cognitive-Emotional domains of the PDDM were associated with baseline disability.ConclusionsThe PDDM model showed limited prognostic value in our context but provided valuable insights into the bio-psycho-social dimensions contributing to disability in CLBP patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".