Multi-task exposure assessment to infer musculoskeletal disorder risk: A scoping review of injury causation theories and tools available to assess exposures
Bibliographic record
Abstract
Exposure assessment is critical for understanding musculoskeletal disorder (MSD) risk. Previous reviews summarized the tools available for single-task exposure assessment, however no reviews summarize tools available to assess the accumulation or aggregation of exposure associated with the performance of multiple tasks (i.e., multi-task assessment). We address this gap by using a scoping review methodology to: 1) summarize the theories explaining how multi-task exposures may lead to MSDs, and 2) summarize the models and tools available to assess multi-task exposures, stratified based on prevailing theories. Using a systematic search strategy, 3230 articles were identified, of which 34 were retained for data extraction. Of the retained articles, 13 described MSD causation theories, 12 described mathematical models (not yet accessible as tools), six described readily accessible tools, and three described both theories and a model or tool. We summarized the state-of-the-art in multi-task exposure assessment and highlight the need for more tools that assess muscle fatigue and inform on recovery.
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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 | 0.023 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".