Propensity-Adjusted Comparison of Mortality of Elderly Versus Very Elderly Ventilated Patients
Bibliographic record
Abstract
BACKGROUND: The growing proportion of elderly intensive care patients constitutes a public health challenge. The benefit of critical care in these patients remains unclear. We compared outcomes in elderly versus very elderly subjects receiving mechanical ventilation. METHODS: In total, 5,557 mechanically ventilated subjects were included in our post hoc retrospective analysis, a subgroup of the VENTILA study. We divided the cohort into 2 subgroups on the basis of age: very elderly subjects (age ≥ 80 y; n = 1,430), and elderly subjects (age 65–79 y; n = 4,127). A propensity score on being very elderly was calculated. Evaluation of associations with 28-d mortality was done with logistic regression analysis. RESULTS: Very elderly subjects were clinically sicker as expressed by higher SAPS II scores (53 ± 18 vs 50 ± 18, P < .001), and their rates of plateau pressure < 30 cm H 2 O were higher, whereas other parameters did not differ. The 28-d mortality was higher in very elderly subjects (42% vs 34%, P < .001) and remained unchanged after propensity score adjustment (adjusted odds ratio 1.31 [95% CI 1.16–1.49], P < .001). CONCLUSIONS: Age was an independent and unchangeable risk factor for death in mechanically ventilated subjects. However, survival rates of very elderly subjects were > 50%. Denial of critical care based solely on age is not justified. (ClinicalTrials.gov registration NCT02731898.)
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".