An evaluation of predictive methods for estimating cumulative spinal loading
Bibliographic record
Abstract
The focus of this study was to assess the amount of error present in several approaches that have been commonly used to estimate the cumulative spinal loading during manual materials handling tasks. Three male subjects performed three sagittal plane lifting tasks of varying loads and postural requirements. Video recordings of the tasks were digitized and a biomechanical model was used to calculate the spinal loading (compression, joint shear, reaction shear, and flexion/extension moment) at L4/L5 for each frame of data. The 'gold standard' for cumulative loading experienced by the subjects was obtained by integrating the resultant biomechanical model outputs for the entire lifting cycle. Five approaches that quantify cumulative spinal loading, four that use discrete measures and one that reduces the number of frames used (5 Hz), were used and compared with the gold standard. The four methods using discrete measures to quantify the cumulative demands of a task resulted in substantial errors (average error across task and subjects was 27-69%). Reducing the number of frames of data processed to 5 frames/s preserved the time varying information and was the only approach examined that did not induce significant error into the cumulative loading estimates. This study indicates that errors in cumulative spinal loading estimates can be large depending upon the approach used, which will hinder any progress in developing a dose-response link between cumulative exposure and an increased risk of low-back pain or injury.
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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 | 0.015 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".