Care trajectories for musculoskeletal disorders following a new episode of low back pain
Bibliographic record
Abstract
ABSTRACT: This study explored diverse care trajectories (CTs) for low back pain (LBP) and other musculoskeletal disorders (MSDs), over a 5-year period following a first episode of LBP. Based on Quebec's administrative health data from 2007 to 2011, this longitudinal cohort study involved 12,608 adults seeking health care for LBP. Using a new multidimensional state sequence analysis, we identified 6 distinct types of CTs. The most prevalent types 1, 2, and 3 (comprising 79.2%, 18.0%, and 21.7% of the cohort, respectively) exhibit rapid recovery and similar patterns of healthcare use over 5 years but differing in initial diagnoses: nonspecific LBP in type 1, trauma-related LBP in type 2 (mostly younger men and highest initial emergency consultation), and specific LBP in type 3. Types 4 to 6, representing smaller groups, show high healthcare utilization with comparable mixed LBP diagnoses at entry but distinctive subsequent care use patterns. Patients in types 4 and 6 (mainly older age groups and women) sought care for other MSDs from general practitioners or specialists, while middle-aged patients in type 5 experienced persistent nonspecific LBP with frequent general practitioner consultations over 5 years. The CTs typology revealed several key areas for improvement in nonpharmacological interventions, including the need to address possible inappropriate medical imaging and invasive interventions for older women with MSDs and the lack of ambulatory care access for younger patients with trauma-related LBP. Finally, results clearly highlighted poor access to rehabilitation physicians and rehabilitation services for all patients suffering from LBP and MSDs.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".