Use of Cold Fluids in Postcardiac Arrest Therapeutic Hypothermia: A Safety Analysis
Bibliographic record
Abstract
Therapeutic hypothermia (TH) has been part of the standard care of postresuscitation patients for more than a decade. Multiple cooling methods are available, including the administration of cold intravenous (IV) fluids. Although this method is widely used, the safety of administration of large volumes of cold IV fluids has not been clearly demonstrated in the literature, and recent evidence points to potential deleterious effects associated with administration of large IV fluid volumes. We conducted a retrospective cohort study among patients who have been treated with TH after cardiac arrest between November 2011 and November 2013 at a tertiary care hospital in Sherbrooke, Quebec, Canada. The primary outcome was the effect of IV fluid quantity on the 28-day survival rate. We reviewed 29 cases, with a total 28-day surviving rate of 51.7%. After adjusting for confounding variables, 28-day surviving rate was not significantly associated with the amount of fluids administrated (odds ratio = 1.034; confidence interval 95% [0.741-1.464]; p = 0.85). The amount of fluids did not influence the variation of the pulmonary component of the sequential organ failure assessment score between days 1 and 3 (ρ = -0.2, p = 0.34). Despite a small sample of patients, cold IV fluids in TH appear safe in the postcardiac arrest population. These findings should be reproduced in a larger, prospective study.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".