A polynomial equation to predict low back compression force: accounting for the effects of load height on instability
Bibliographic record
Abstract
The purpose of this study was to develop a regression equation that, incorporating the potential energy of the load in the hands, was capable of improved predictions of spinal compression forces. A stepwise polynomial equation was developed from EMG profiles of 15 muscles, and its spinal joint loading predictions at L4/L5 were compared to current methods of calculating spinal compression. Absolute muscle activation was shown to increase with increased loading height, indicating that the central nervous system responds to changes in spinal stability. The inclusion of potential energy into the calculation of spinal disc compression at L4/L5 improved estimates of the compressive forces acting on the spine. This is the first model to incorporate potential energy into a predictive model for lumbar spine compression without the use of electromyography. It was concluded that potential energy plays a vital role in dictating the recruitment patterns of the trunk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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; a candidate call from one teacher head, 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".