The Need for Multidimensional Stratification of Chronic Low Back Pain (LBP)
Bibliographic record
Abstract
MINI: The authors wanted to determine which existing primary-care low back pain stratification schema is associated with distinct subpopulations. Initial stratification by DMPP identified potentially distinct epidemiological groups. DMPP stratification resulted in discrimination beyond that provided by disability or chronicity risk stratification alone. STUDY DESIGN: A cross-sectional study of Canadian patients suffering from low back pain (LBP) seeking primary care. OBJECTIVE: The aim of this study was to determine which existing primary care LBP stratification schema is associated with distinct subpopulations as characterized by easily identifiable primary epidemiological factors. SUMMARY OF BACKGROUND DATA: LBP is among the most frequent reasons for visits to primary care physicians and a leading cause of years lived with disability. In an effort to improve treatment response/outcomes in LBP primary care, different classification systems have been proposed in an effort to provide more tailored treatment with the intent of improving outcomes. Group-specific risk factors and underlying etiology might suggest a need for, or inform, changes to treatment approaches to optimize LBP outcomes. METHODS: Stratification by dominant mechanical pain patterns; chronicity risk; disability severity. Multinomial logistic regression was used to identify the system showing greatest variability in associations with age, sex, obesity, and comorbidity. Once identified, the remaining schemas were incorporated into the model. RESULTS: N = 970; mean age: 50 years (range: 18-93); 56% female. Stratification by pain pattern revealed greater variability. Adjusted analysis: Increasing age was associated with greater odds of intermittent, extension-based back- or leg-dominant pain [odds ratio (OR): 1.02 and 1.06; P < 0.01]; being male with leg-dominant pain (ORs > 2; P < 0.01). Overweight/obesity was associated with extension-based leg-dominant pain (OR = 2.6; P < 0.02) and increasing comorbidity with extension-based back-dominant pain (OR = 1.3; P < 0.01). Severe disability was associated only with constant leg pain (OR = 3.9; P < 0.01), and high chronicity risk with extension-based leg-dominant pain (OR = 0.4; P = 0.03). CONCLUSION: Dominant mechanical symptom stratification resulted in further discrimination of an epidemiologically distinct and a large subgroup of LBP patients not identified by disability or chronicity risk stratification alone. Findings suggest a need for primary care initiated multidimensional stratification in chronic LBP. LEVEL OF EVIDENCE: 3.
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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 | 0.019 | 0.037 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".