Contribution of Nonspinal Comorbidity to Low Back Pain Outcomes
Bibliographic record
Abstract
OBJECTIVE: To determine the involvement of comorbidity to outcomes in a cohort of acute mechanical low back pain patients. METHODS: Incident low back pain cases (n=7077) in the acute or subacute phase assessed between January 1, 1999 and December 31, 2001 were included. Patients were categorized into 1 of 2 groups on the basis of their current medical history: (1) those with at least 1 of 7 medical histories considered (Comorbidity Group, n=539), or (2) those with only low back pain (Back Pain Group, n=6538). Main outcome measures were: change in perceived function and visual analog scale (VAS) pain rating from initial assessment to discharge, and total number of treatment days. RESULTS: There were no baseline statistically significant differences in VAS pain rating, questionnaire score, or symptom duration between groups. Odds ratios (ORs) were adjusted to reflect age and sex differences between groups. Logistic regression analysis revealed no statistically significant difference for change in functional score (OR=1.002) between groups; there were marginal differences in change in VAS pain rating (OR=1.08) and total number of treatment days (OR=1.006). chi analysis revealed no statistically significant differences in medication use, global pain rating, or pain control ability posttreatment, between groups. DISCUSSION: Significant ORs were barely greater than 1.00 and were likely the result of the large sample size. The clinical course for comorbid patients, who may seem more complicated at the start of treatment, is just as favorable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".