Bibliographic record
Abstract
Objectives In this study we examined which factors best predict return to work for workers away from work due to acute low back pain. Based on the International Classification of Functioning, Disability and Health, we distinguish between factors related to LBP, to the worker, to the job and to the psychosocial environment that influence duration of an episode of being off work. We updated a previous review because of advances in the field. Methods PubMed, EMBASE, and PsycINFO were searched up to March 2011. Quality of the studies was assessed on 19 methodological items. Levels of evidence will be determined and results will be pooled if possible. Results 4947 titles and abstracts were retrieved and after screening of title abstract and papers for inclusion and exclusion criteria, 28 relevant publications from 25 studies were identified. Two studies that were selected in the previous review were excluded after contact with the authors and due to stricter criteria. Studies were from: Belgium: 2, The Netherlands: 7, USA: 10, Canada: 3, Norway: 2, and Greece: 1. After initial disagreement (overall ICC=0.63) consensus was reached on quality. The average quality of studies was 12.21 with a minimum score of 6 and a maximum score of 16. Approximately 220 factors were considered in these studies. Preliminary analysis shows that recovery expectations, radiating pain, disability and pain might be important factors. Conclusions The review raises the issue of more theory based/biological plausible studies. At the conference we will present pooled results and feedback from stakeholders on relevance.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".