The Association between Obesity and Outcomes in Critically Ill Patients
Bibliographic record
Abstract
BACKGROUND: Obesity rates are increasing worldwide, particularly in North America. The impact of obesity on the outcome of critically ill patients is unclear. METHODS: A prospective observational cohort study of consecutive patients admitted to a tertiary critical care unit in Canada between January 10, 2008 and March 31, 2009 was conducted. Exclusion criteria were age <18 years, admission <24 h, planned cardiac surgery, pregnancy, significant ascites, unclosed surgical abdomen and brain death on admission. Height, weight and abdominal circumference were measured at the time of intensive care unit (ICU) admission. Coprimary end points were ICU mortality and a composite of ICU mortality, reintubation, ventilator-associated pneumonia, line sepsis and ICU readmission. Subjects were stratified as obese or nonobese, using two separate metrics: body mass index (BMI) ≥ 30 kg/m(2) and a novel measurement of 75th percentile for waist-to-height ratio (WHR). RESULTS: Among 449 subjects with a BMI ≥ 18.5 kg/m(2), both BMI and WHR were available for comparative analysis in 348 (77.5%). Neither measure of obesity was associated with the primary end points. BMI ≥ 3 0 kg/m(2) was associated with a lower odds of six-month mortality than the BMI <30 kg/m(2) group (adjusted OR 0.59 [95% CI 0.36 to 0.97]; P=0.04) but longer intubation times (adjusted RR 1.56 [95% CI 1.17 to 2.07]; P=0.003) and longer ICU length of stay (adjusted RR 1.67 [95% CI 1.21 to 2.31]; P=0.002). Conversely, measurement of 75th percentile for WHR was associated only with decreased ICU readmission (OR 0.23 [95% CI 0.07 to 0.79]; P=0.02). CONCLUSIONS: Obesity was not necessarily associated with worse outcomes in critically ill patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".