Developing common metrics of mechanical exposures across aetiological studies of low back pain in working populations for use in meta-analysis
Bibliographic record
Abstract
OBJECTIVES: One of the challenges of conducting meta-analyses on the relationship between workplace mechanical exposures and low back pain is that mechanical exposures are reported in a wide variety of ways. We aimed to develop common metrics to apply in the translation of literature-based workplace mechanical exposures for use in meta-analyses, and to test the metrics' measurement properties. METHODS: We developed a set of 7-point scales to capture the intensity of important aspects of mechanical exposures that may be related to the development of low back pain in workers. The scales represented three dimensions of mechanical exposures at work: (1) trunk posture, (2) weight lifted or force exerted and (3) spinal loading, and estimated both peak and cumulative loads. Measurement properties of the scales were tested through a survey of experts in biomechanics and ergonomics who were asked to rate literature-based workplace exposure definitions using the scales and provide estimates of their confidence in their ratings. RESULTS: For each dimension the ratings for peak loads tended to be higher than the cumulative load ratings. The inter-rater reliability for the scales ranged from 0.3 to 0.5; we would need to average the ratings of at least four expert raters to have an acceptable level of reliability (>0.7). Inter-expert reliability was positively related to the experts' level of confidence in their ratings. In most cases the ranking of intensity ratings from the experts matched the ranking of exposure intensity from the original articles. CONCLUSIONS: This study provides insight into estimating the intensity of literature-based mechanical exposure metrics using a common set of scales which can be applied across epidemiologic studies. These metrics may be useful to quantify the relationship between workplace mechanical exposure and low back pain in a systematic review and meta-analysis.
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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 | 0.371 | 0.602 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.045 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".