0440 Shoulder repetitiveness exposure threshold detection using adjusted hazard multivariate parametric modelling for cumulative trauma disorders (ctd) prevention and causal assessment
Bibliographic record
Abstract
Background Cumulative-Trauma Disorders are major loss causes in labour environments through the world, but few is known about quantitative workload exposure limits. The aim of this research was to define shoulder repetitiveness exposure threshold by assessing the risk of rotator cuff, biceps and bursal injuries in a cohort of workers. Methods A retrospective cohort study was assembled with workers from different positions. Inclusion/exclusion criteria were rigorously applied. Clinical variables were extracted from each worker clinic history; dependent variable was obtained using NMR, ultrasound and/or surgical reports. Shoulders workload was assessed independently getting cumulative exposure time to repetitive motions adjusting by rest/break periods and other covariates, controlling confusing effects. The exposure threshold was acquired using the ”David´s cheese bread” method with an adjusted multivariate Weibull regression modelling, previously adopting Akaike Criterion. A Huber’s M-estimator was performed warranting robust estimators for correcting both shoulders non-completely independent measures (two shoulders by worker). Final model was built according with Hosmer-Lemeshow-May´s covariates purposeful selection principles. Findings/conclusions 328 workers (656 shoulders) were included (95,8% sample power). At following-end, following span median was 21.6 years, age median was 42 years, 60% were women, 85% had non-university academic level and 77% had non-administrative positions. Age, handedness, academic level, work type and mood disorders were proved as significant or as confounding covariates within the final model. Cumulative 4 × 103 effective working hours for shoulder repetitiveness exposure was established as threshold with adjusted HRR=1.93 (95%CI 1.04–3.59). No worker should be exposed more than that threshold in order to eliminate shoulder´s CTD.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".