Amount of Care per Survivor in Young and Older Patients Hospitalized in Intensive Care Unit: A Retrospective Study*
Bibliographic record
Abstract
BACKGROUND: It is unknown whether the amount of care deployed in the intensive care unit population divided by the number of survivors, that is, amount of care per survivor including the care performed for nonsurvivors, differs between patients older and younger than 75 years of age. METHODS: Data were extracted from the computerized files of all 2,220 patients admitted in a medical intensive care unit between January 2009 and December 2010. Patients ≥75 and <75 years old were compared. The Omega score per survivor (OMEGA/S) was calculated in both age groups by dividing the total amount of Omega points, a score of cumulated care load calculated over intensive care unit stay, by the number of survivors in each group. RESULTS: OMEGA/S was 26% higher in elderly versus younger patients when considering intensive care unit mortality and 40% higher when considering hospital mortality. The absence of difference in raw Omega values between the two groups implies that OMEGA/S differences were related to differences in mortality rate. Simplified Acute Physiology Score II (without age-related points) strata analysis (<20, 20-39, 40-59, 60-79, and ≥80) showed that OMEGA/S in the elderly patients was significantly higher in the first three Simplified Acute Physiology Score II strata only. When calculating by main diagnosis categories, a major increase in the difference of OMEGA/S between elderly and younger patients was observed in cardiac arrest patients due to a major difference in mortality rate. CONCLUSIONS: Elderly patients required a significantly higher care load per survivor in comparison to younger patients. This excess was mainly due to patients with low initial severity.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".