Effect of hyperthermia on simulated muscle activation in female when crossing obstacle
Bibliographic record
Abstract
Abstract It is well known that hyperthermia greatly impairs neuromuscular function and dynamic balance. However, whether a greater level of hyperthermia could potentially alter the lower limb simulated muscle activation when crossing an obstacle in female participants remains unknown. Therefore we examined the effect of a systematic increase in oral temperature on lower limb simulated muscle activation when crossing an obstacle in female participants. Eighteen female participants were recruited where they underwent a control trial (Con) and two progressive passive heating trials with Δ 1°C and Δ 2°C increase of oral temperature (Toral) using a 45°C water bath. In each trial, we assessed lower limb simulated muscle activation when crossing an obstacle height of 10%, 20%, and 30% of the participant’s leg length and toe-off, toe-above-obstacle and heel-strike events were identified and analyzed. In all events, the lower limb simulated muscle activation were greater in Δ2°C than Δ1°C and Con when both leading and trailing limbs crossed the obstacle height of 20% and 30% leg length (all p < 0.001). However, the lower limb simulated muscle activation were not different between Δ1°C and Con across all obstacle heights (p > 0.05). This study concluded that a greater level of hyperthermia resulted in a greater lower limb simulated muscle activation to ensure safety and stability when females cross an obstacle height of 20% leg length or higher.
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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) | 0.003 | 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".