Predicting 3D cumulative L4/L5 spine loads using heart rate determined physical activity level
Bibliographic record
Abstract
The purpose of this study was to predict 3D cumulative L4/L5 spine loads and moments incurred during non-occupational tasks, from heart rate determined physical activity level (HR-PAL). Twelve subjects were videotaped while performing activities in their own homes. HR was continuously recorded during video collection and was subsequently used to calculate the PAL over the course of the 2-hour collection session. Simple regression revealed that between 76% and 82% of the variance in 3 of the 13 cumulative load measures studied (cumulative compression force and cumulative flexion and right axial twist moments) was accounted for by HR-PAL. Four additional cumulative output variables approached statistical significance. Cumulative compression force was the best predicted of all measures studied. Predicted and actual loads were not different from each other for all significant load measures. This initial study suggests that the use of heart rate for predicting cumulative compression shows potential as a simple method to track extended periods of cumulative exposure. Future work is planned to test this method in a number of industrial settings.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".