Prehospital cooling by cold infusion: searching for the optimal infusion regimen
Bibliographic record
Abstract
BACKGROUND: The cooling efficacy of intravenous administration of cold crystalloids can be enhanced by optimisation of the procedure. This study assessed the temperature stability of different application regimens of cold normal saline (NS) in simulated prehospital conditions. METHODS: Twelve different application regimens of 4 °C cold NS (volumes of 250, 500 and 1000 ml applied at infusion rates of 1000, 2000, 4000 and 6000 ml/h) were investigated for infusion temperature changes during administration to an artificial detention reservoir in simulated prehospital conditions. RESULTS: An increase in infusion temperature was observed in all regimens, with an average of 8.1 ± 3.3 °C (p<0.001). This was most intense during application of the residual 20% of the initial volume. The lowest rewarming was exhibited in regimens with 250 and 500 ml bags applied at an infusion rate of 6000 ml/h and 250 ml applied at 4000 ml/h. More intense, but clinically acceptable, rewarming presented in regimens with 500 and 1000 ml bags administered at 4000 ml/h, 1000 ml at 6000 ml/h and 250 ml applied at 2000 ml/h. Other regimens were burdened by excessive rewarming. CONCLUSION: Rewarming of cold NS during application in prehospital conditions is a typical occurrence. Considering that the use of 250 ml bags means the infusion must be exchanged too frequently during cooling, the use of 500 or 1000 ml NS bags applied at an infusion rate of ≥4000 ml/h and termination of the infusion when 80% of the infusion volume has been administered is regarded as optimal.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".