Severe Hypothermia Increases the Risk for Intensive Care Unit-Acquired Infection
Bibliographic record
Abstract
BACKGROUND: Although hypothermia is widely accepted as a risk factor for subsequent infection in surgical patients, it has not been well defined in medical patients. We sought to assess the risk of acquiring intensive care unit (ICU)--acquired infection after hypothermia among medical ICU patients. METHODS: Adults (≥18 years) admitted to French ICUs for at least 2 days between April 2000 and November 2010 were included. Surgical patients were excluded. Patient were classified as having had mild hypothermia (35.0°C-35.9°C), moderate hypothermia (32°C-34.9°C), or severe hypothermia (<32°C), and were followed for the development of pneumonia or bloodstream infection until ICU discharge. RESULTS: A total of 6237 patients were included. Within the first day of admission, 648 (10%) patients had mild hypothermia, 288 (5%) patients had moderate hypothermia, and 45 (1%) patients had severe hypothermia. Among the 5256 patients who did not have any hypothermia at day 1, subsequent hypothermia developed in 868 (17%), of which 673 (13%), 176 (3%), and 19 (<1%) patients had lowest temperatures of 35.0°C-35.9°C, 32.0°C-34.9°C, and <32°C, respectively. During the course of ICU admission, 320 (5%) patients developed ICU-acquired bloodstream infection and 724 (12%) patients developed ICU-acquired pneumonia. After controlling for confounding variables in multivariable analyses, severe hypothermia was found to increase the risk for subsequent ICU-acquired infection, particularly in patients who did not present with severe sepsis or septic shock. CONCLUSIONS: The presence of severe hypothermia is a risk factor for development of ICU-acquired infection in medical patients.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".