Rehabilitation management of low back pain – it’s time to pull it all together!
Bibliographic record
Abstract
Yannick Tousignant-Laflamme,1,2 Marc Olivier Martel,3 Anand B Joshi,4 Chad E Cook5 1School of Rehabilitation, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada; 2Clinicial Research Center of the Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada; 3Faculties of Dentistry & Medicine, McGill University, Montreal, QC, Canada; 4Duke Health, Department of Orthopaedic Surgery, Duke University Medical Center, 5Doctor of Physical Therapy Division, Duke University, Durham, NC, USA Abstract: In the past, rehabilitation research initiatives for low back pain (LBP) have targeted outcome enhancement through personalized treatment approaches, namely through classification systems (CS). Although the use of CS has enhanced outcomes, common management practices have not changed, the prevalence of LBP is still high, and only selected patients meet the CS profile, namely those with a nociceptive context. Similarly, although practice guidelines propose some level of organization and occasionally a timeline of care provision, each mainly provides best practice for isolated treatment approaches. Moreover, there is no theoretical framework that has been proposed that guides the rehabilitation management process of mechanical LBP. In this commentary, we propose a model constituted of five domains (nociceptive drivers, nervous system dysfunction drivers, comorbidities drivers, cognitive–emotional drivers, and contextual drivers) grounded as mechanisms driving pain and/or disability in LBP. Each domain is linked to the International Classification of Functioning, Disability and Health, where once a patient is deemed suitable for rehabilitation, the clinician assesses elements of each domain in order to identify where the relative treatment efforts should be focused. This theoretical model is designed to provide a more comprehensive management overview, by appreciating the relative contribution of each domain driving pain and disability. Considering that the multiple domains driving pain and disability, and their interaction, requires a model that is comprehensive enough to identify and address each related issue, we consider that the proposed model has several positive implications for rehabilitation of this painful and highly prevalent musculoskeletal disorder. Keywords: low back pain, spine, rehabilitation, pain management
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".