Differences among primary care patients with different mechanical patterns of low back pain: a cross-sectional investigation
Bibliographic record
Abstract
OBJECTIVES: To characterise and compare a persistent low back pain (LBP) population based on 4 clinical pain patterns. DESIGN: Cross-sectional analysis of patient-reported data. SETTING: Patients from 220 primary care practitioners in 3 cities in Ontario, Canada. PARTICIPANTS: 1020 individuals seeking LBP care. INCLUSION CRITERIA: LBP symptoms lasting 1½-12 months, or unmanageable recurrent symptoms; ages 18+years. EXCLUSION CRITERIA: pregnant/1-year postpartum; involved in active litigation or motor vehicle injury; emergent spinal presentations; pain disorder diagnosis; work injury claim; or constant symptoms persisting >12 months postonset. MAIN OUTCOME MEASURE: Hall pain pattern subgroups: back dominant pain aggravated by flexion (P1) or extension (P2), or leg dominant constant (P3) or intermittent (P4) pain (multinomial logistic outcome; referent: P1). RESULTS: Groups P1 and P2 had the highest proportion of women. P2 and P4 had higher mean ages and comorbidity counts. P3 and P4 had higher proportions of overweight/obese individuals and lower general health scores. Adjusted models: being male and overweight/obese was associated with increased odds of being in P3 (OR 1.64 (95% CI 1.10 to 2.46), and OR 1.74 (1.13 to 2.68), respectively) and P4 (OR 1.87 (1.11 to 3.15) and OR 1.91 (1.06 to 3.42), respectively), and increasing age with increased odds of being in P2 (OR 1.02 (1.01 to 1.03)) and P4 (OR 1.06 (1.04 to 1.08)). Increasing comorbidity count was associated with increased odds of being in P2 (OR 1.14 (1.0 to 1.3)), and better general health scores with decreased odds of being in P3 (OR 0.40 (0.18 to 0.93)). CONCLUSIONS: This is the first study to examine the 'Hall system' in a non-rehab primary care population. Subgroups classified according to this system appear to have distinct profiles. Further research is needed to better characterise and determine the prognostic implication of these clinically derived subgroups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".