Benchmarking clinical management of spinal and non-spinal disorders using quality of life: results from the EPI3-LASER survey in primary care
Bibliographic record
Abstract
Concerns have been raised regarding sub-optimal utilization of analgesics and psychotropic drugs in the treatment of patients with chronic musculoskeletal disorders (MSDs) and their associated co-morbidities. The objective of this study was to describe drug prescriptions for the management of spinal and non-spinal MSDs contrasted against a standardized measure of quality of life. A representative population sample of 1,756 MSDs patients [38.5% with spinal disorder (SD) and 61.5% with non-spinal MSDs (NS-MSD)] was drawn from the EPI3-LASER survey of 825 general practitioners (GPs) in France. Physicians recorded their diagnoses and prescriptions on that day. Patients provided information on socio-demographics, lifestyle and quality of life using the Short Form 12 (SF-12) questionnaire. Chronicity of MSDs was defined as more than 12 weeks duration of the current episode. Chronic SD and NS-MSD patients were prescribed less analgesics and non-steroidal anti-inflammatory drugs than their non-chronic counterpart [odds ratios (OR) and 95% confidence intervals (CI), respectively: 0.4, 0.2-0.7 and 0.5, 0.3-0.6]. They also had more anxio-depressive co-morbidities reported by their physicians (SD: 16.1 vs.7.4%; NS-MSD: 21.6 vs. 9.5%) who prescribed more antidepressants and anxiolytics with a difference that was statistically significant only for spinal disorder patients (OR, 95% CI: 2.0, 1.1-3.6). Psychotropic drugs were more often prescribed in patients in the lower quartile of SF-12 mental score and prescriptions of analgesics in the lower quartile of SF-12 physical score (P < 0.001). In conclusion, anxiety and depressive disorders were commonly reported by GPs among chronic MSD patients. Their prescriptions of psychotropic and analgesic drugs were consistent with patients' self-rated mental and physical health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".