Adoption of the use of a reflective blanket for passive re-warming of the stable mildly hypothermic patient to reduce unnecessary ICU admissions: A pilot study
Bibliographic record
Abstract
Background and objective: In adult inpatients, thermoregulation is important to support vital functions. Occasionally, due to procedures or benign clinical conditions, such as hypoglycemia, autonomic instability, brain injury, paraplegia, autonomic dysreflexia, and septic shock, patients’ body temperature falls below 35°C and thus, requires intervention. Due to the floor nursing constraints of hourly monitoring of forced air re-warming, these active warming techniques often lead to clinically unnecessary admissions to intensive care units and utilization of scarce and costly resources. The purpose of this pilot study was to explore passive re-warming with alternative technologies to avoid unnecessary admissions to the intensive care unit.Methods: A pilot study based on a sample of seventeen patients was conducted to assess the adoption and associated outcome of the use of the reflective blanket. Intervention: Application of a reflective blanket to patients who were mildly hypothermic, yet clinically stable, was explored as an effective mechanism to re-warm these patients in the non-intensive care unit setting. Results: The investigation based on the use of a reflective blanket on a sample of seventeen hypothermic patients had a success rate of 0.7059 (70.59%).Conclusions: We conclude that the use of a reflective blanket is an effective and safe passive rewarming mechanism that leads to avoidance of unnecessary intensive care unit admissions. This leads to both cost reductions to patients and appropriate use of ICU resources.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".