Can We Identify People at Risk of Non-recovery after Acute Occupational Low Back Pain? Results of a Review and Higher-Order Analysis
Bibliographic record
Abstract
PURPOSE: To identify prognostic factors in the literature that may predict a poor recovery from acute occupational low back pain (LBP). METHODS: Four international databases (Medline, CINAHL, EMBASE, and PsycINFO) were reviewed, searching all articles indexed up to November 2007 with the term low back pain combined with the terms prognostic, prospective, or cohort. Following application of inclusion criteria, 10 articles were found to be appropriate for data extraction. Each article was critically appraised by two independent reviewers. Statistical pooling was performed on any factor evaluated in at least three independent cohorts. RESULTS: Seven cohorts were identified, with a total sample size of 2,484 subjects. Only three factors were followed in at least three cohorts and were therefore suitable for statistical pooling: female gender (OR=1.28, 95% CI: 1.03-1.58); pain radiation (OR=1.37, 95% CI: 0.79-2.39); and previous history of back pain (OR=0.91, 95% CI: 0.52-1.60). There was significant heterogeneity within the female gender factor; compensation of subjects for study participation appeared to moderate its effect. CONCLUSION: After statistical pooling, only female gender achieved statistical significance as a prognostic factor for prolonged recovery. Further research is necessary to determine prognostic factors for non-recovery in acute LBP.
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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 | 0.017 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".