Cold-water immersion and iced-slush ingestion are effective at cooling firefighters following a simulated search and rescue task in a hot environment
Bibliographic record
Abstract
Firefighters are exposed to hot environments, which results in elevated core temperatures. Rapidly reducing core temperatures will likely increase safety as firefighters are redeployed to subsequent operational tasks. This study investigated the effectiveness of cold-water immersion (CWI) and iced-slush ingestion (SLUSH) to cool firefighters post-incident. Seventy-four Australian firefighters (mean ± SD age: 38.9 ± 9.0 years) undertook a simulated search and rescue task in a heat chamber (105 ± 5 °C). Testing involved two 20-min work cycles separated by a 10-min rest period. Ambient temperature during recovery periods was 19.3 ± 2.7 °C. Participants were randomly assigned one of three 15-min cooling protocols: (i) CWI, 15 °C to umbilicus; (ii) SLUSH, 7 g·kg(-1) body weight; or (iii) seated rest (CONT). Core temperature and strength were measured pre- and postsimulation and directly after cooling. Mean temperatures for all groups reached 38.9 ± 0.9 °C at the conclusion of the second work task. Both CWI and SLUSH delivered cooling rates in excess of CONT (0.093 and 0.092 compared with 0.058 °C·min(-1)) and reduced temperatures to baseline measurements within the 15-min cooling period. Grip strength was not negatively impacted by either SLUSH or CONT. CWI and SLUSH provide evidence-based alternatives to passive recovery and forearm immersion protocols currently adopted by many fire services. To maximise the likelihood of adoption, we recommend SLUSH ingestion as a practical and effective cooling strategy for post-incident cooling of firefighters in temperate regions.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".