Active and passive heat stress similarly compromises tolerance to a simulated hemorrhagic challenge (1104.22)
Bibliographic record
Abstract
Passive heat stress increases core and skin temperatures and reduces tolerance to simulated hemorrhage, as assessed via lower body negative pressure (LBNP). It is unknown whether exercise induced heat stress reduces LBNP tolerance to a similar extent relative to passive heat stress. Eight males (32 ± 7 yrs, 176 ± 8 cm, 77.0 ± 9.8 kg) underwent LBNP to pre‐syncope on three separate and randomized occasions: passive heat stress and two occasions of exercise induced heat stress where skin temperature was moderate (~36°C, Active 36) or warm (~38°C, Active 38). LBNP tolerance was quantified using the cumulative stress index (CSI). Prior to LBNP, increases in core temperature were similar in all trials (1.18 ± 0.20°C; P < 0.05), and while mean skin temperature was similar between passive heat stress (38.2 ± 0.5°C) and Active 38 (38.2 ± 0.8°C; P = 0.90) both trials were greater compared to Active 36 (36.6 ± 0.5°C; P < 0.05). LBNP tolerance was not different between passive heat stress and Active 38 (383 ± 223 and 322 ± 178 CSI; P = 0.12) but both were similarly reduced relative to the Active 36 (516 ± 147 CSI, both P < 0.05). At pre‐syncope mean arterial pressure and heart rate were not different between trials. These data suggest that tolerance to a simulated hemorrhagic challenge is similar following passive and exercise induced heat stress when skin temperatures are similarly elevated. Grant Funding Source : DOD: W81XWH‐09‐2‐0194 and NIH: HL61388
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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 | 0.000 |
| 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 | 0.001 |
| 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".