Noninvasive assessment of muscle temperature during rest, exercise, and postexercise recovery in different environments
Bibliographic record
Abstract
We introduced noninvasive and accurate techniques to estimate muscle temperature (Tm) of vastus lateralis (VL), triceps brachii (TB), and trapezius (TRAP) during rest, exercise, and postexercise recovery using the insulation disk (iDISK) technique. Thirty-six volunteers (24 men, 12 women; 73.0 ± 12.2 kg; 1.75 ± 0.07 m; 24.4 ± 5.5 yr; 49.2 ± 6.8 ml·kg(-1)·min(-1) peak oxygen uptake) underwent periods of rest, cycling exercise at 40% of peak oxygen uptake, and postexercise recovery in three environments: Normal (24°C, 56% relative humidity), Hot-Humid (30°C, 60% relative humidity), and Hot-Dry (40°C, 24% relative humidity). Participants were randomly allocated into the "model" and the "validation" groups. Results in the model group demonstrated that Tm (VL: 36.65 ± 1.27°C; TB: 35.76 ± 1.73°C; TRAP: 36.53 ± 0.96°C) was increased compared with iDISK (VL: 35.67 ± 1.71°C; TB: 34.77 ± 2.27°C; TRAP: 35.98 ± 1.34°C) across all environments (P < 0.001). Stepwise regression analysis generated models that accurately predicted Tm (predTm) of VL (R(2) = 0.73-0.91), TB (R(2) = 0.85-0.93), and TRAP (R(2) = 0.84-0.86) using iDISK and the difference between the current iDISK temperature and that recorded between 1 and 4 min before. Cross-validation analyses in the validation group demonstrated small differences (P < 0.05) of no physiological significance, small effect size of the differences, and strong associations (r = 0.85-0.97; P < 0.001) between Tm and predTm. Moreover, narrow 95% limits of agreement and low percent coefficient of variation were observed between Tm and predTm. It is concluded that the developed noninvasive, practical, and inexpensive techniques provide accurate estimations of VL, TB, and TRAP Tm during rest, cycling exercise, and postexercise recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".