Regulating Movement Frequency and Speed: Implications for Lumbar Spine Load Management Strategies Demonstrated Using an In Vitro Porcine Model
Bibliographic record
Abstract
The relationship between internal loading dose and low-back injury risk during lifting is well known. However, the implications of movement parameters that influence joint loading rates-movement frequency and speed-on time-dependent spine loading responses remain less documented. This study quantified the effect of loading rate and frequency on the tolerated cumulative loading dose and its relation to joint lifespan. Thirty-two porcine spinal units were exposed to biofidelic compression loading paradigms that differed by joint compression rate (4.2 and 8.3 kN/s) and frequency (30 and 60 cycles per minute). Cyclic compression testing was applied until failure was detected or 10,800 continuous cycles were tolerated. Instantaneous weighting factors were calculated to evaluate the cumulative load and Kaplan-Meier survival probability functions were examined following nonlinear dose normalization of the cyclic lifespan. Significant reductions in cumulative compression were tolerated when spinal units were compressed at 8.3 kN/s (P < .001, 67%) and when loaded at 30 cycles per minute (P = .008, 45%). There was a positive moderate relationship between cumulative load tolerance and normalized cyclic lifespan (R2 = .52), which was supported by joint survivorship functions. The frequency and speed of movement execution should be evaluated in parallel to loading dose for the management of low-back training exposures.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".