Cranial-neck and inhalation rewarming failed to improve recovery from mild hypothermia.
Bibliographic record
Abstract
INTRODUCTION: Rewarming from hypothermia in a field setting is a challenge due to the typical lack of significant power or heat source, making the targeted application of available heat critical. The highly vascular area of the head and neck may allow heat to be rapidly transferred to the core via blood circulation. At the same time, the warming of only a small skin surface may minimize the rapid rise in skin temperature proposed to attenuate shivering and endogenous heat production. Therefore, we investigated the efficacy of targeting the head and neck for rewarming from mild hypothermia. METHODS: There were 16 participants (9 men, 24.1 +/- 4.5 yr, 15.5 +/- 3.9% body fat; 6 women, 23.0 +/- 5.4 yr, 20.8 +/- 3.2% body fat) who were cooled in 15 degrees C water until rectal or esophageal temperature reached 35.5 degrees C, whereupon they were removed and provided passive (PASS), cranial-neck (CN), or cranial-neck and inhalation (CNIR) rewarming. Heart rate and skin temperature were also measured. RESULTS: The mean cooling times were PASS=83 min (range: 22-295 min), CN=94 min (range: 28-314 min), CNIR=97 min (range: 22-285 min). No significant differences (p > 0.05) were found for magnitude of after-drop (PASS = 0.33 +/- 0.24 degrees C, CN = 0.31 +/- 0.18 degrees C, CNIR = 0.29 +/- 0.28 degrees C esophageal temperature) and duration of afterdrop (PASS = 15.4 +/- 10.2 min, CN = 13.0 +/- 10.1 min, CNIR = 8.8 +/- 6.9 min). No significant differences (p > 0.05) were found for rewarming rate (PASS = 1.85 +/- 1.33 degrees C x h(-1), CN = 1.45 +/- 1.04 degrees C x h(-1), CNIR = 2.24 +/- 1.51degrees C x h(-1) esophageal temperature). DISCUSSION: In summary, neither cranial-neck nor cranial-neck and inhalation rewarming combined have an advantage in reducing the magnitude and duration of after-drop or increasing the rewarming rate over passive rewarming.
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 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".