Incremental training intensities increases loads on the lower back of elite female rowers
Bibliographic record
Abstract
Lumbar-pelvic kinematics change in response to increasing rowing stroke rates, but little is known about the effect of incremental stroke rates on changes in joint kinetics and their implications for injury. The purpose of this study was to quantify the effects of incremental rowing intensities on lower limb and lumbar-pelvic kinetics. Twelve female rowers performed an incremental test on a rowing ergometer. Kinematic data of rowers' ankle, knee, hip and lumbar-pelvic joints, as well as external forces at the handle, seat and foot-stretchers of the rowing machine were recorded. Inter-segmental moments and forces were calculated using inverse dynamics and were compared across stroke rates using repeated measures ANOVA. Rowers exhibited increases in peak ankle and L5/S1 extensor moments, reductions in peak knee moments and no change in peak hip moments, with respect to stroke rate. Large shear and compressive forces were seen at L5/S1 and increased with stroke rate (P < 0.05). This coincided with increased levels of lumbar-pelvic flexion. High levels of lumbar-pelvic loading at higher stroke rates have implications with respect to injury and indicated that technique was declining, leading to increased lumbar-pelvic flexion. Such changes are not advantageous to performance and can potentially increase the risk of developing injuries.
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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 | 0.001 |
| 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) | 0.002 | 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".