Perspectives on prevention, assessment, and rehabilitation of low back pain in WORK
Bibliographic record
Abstract
INTRODUCTION: The aim of this review was to describe the low back pain (LBP) knowledge base developed in WORK and to discuss its relevance to current perspectives in the broader literature on LBP and employment. METHOD: A scoping review of the literature in WORK on LBP and employment was conducted using published articles from 1990-2009. Articles were organized into geographical regions and summarized for contributions to the domains of WORK: prevention, assessment, and rehabilitation. Methodological accordance of the articles was also assessed. RESULTS: Fifty articles were extracted and organized into contributions from authors within North America (n=34) and outside North America (n=16). In total there were 26 prevention, 7 assessment, and 12 rehabilitation articles in this review. Five articles were also classified as 'understanding' articles. More than half of the articles retrieved employed quantitative methodology. CONCLUSIONS: WORK has contributed a broad realm of publications to the knowledge base on LBP and employment. Two thirds of the articles were contributed from authors within North America, with a greater emphasis on prevention. This article highlights the similarities and differences in the international knowledge base in the management of LBP in WORK. Future directions for research are elaborated drawing on current perspectives of two experts on the management of 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".