Outcomes among critically ill adults with influenza infection
Bibliographic record
Abstract
Background: Influenza infection is a major cause of mortality in critical care units. Methods: Data on critically ill adult patients with influenza infection from 2014 to 2019 were retrospectively collected, including mortality and critical care resource utilization. Independent predictors of mortality were identified using Cox regression. Results: One hundred thirty patients with confirmed influenza infection had a mean age of 56 (SD 16) years; 72 (55%) were male. Mean Acute Physiology and Chronic Health Evaluation (APACHE II) score was 22 (SD 9). One hundred eight (83%) patients had influenza A (46% H1N1pdm09, 33% H3N2); 21 (16%) had influenza B. Fifty-five (42%) patients had bacterial co-infection. Only 5 (4%) had fungal co-infection. One hundred eight (83%) patients required mechanical ventilation; 94 (72%), vasopressor support; 26 (20%), continuous renal replacement therapy (CRRT); and 11 (9%), extracorporeal membrane oxygenation. One hundred twenty one (93%) patients received antiviral therapy (median 5 d). Thirty-day mortality was 23%. Patients who received antiviral treatment were more likely to survive with an adjusted hazard ratio (aHR) of 0.15 (95% CI 0.04 to 0.51, p = 0.003). Other independent predictors of mortality were the need for CRRT (aHR 2.48, 95% CI 1.14 to 5.43, p = 0.023), higher APACHE II score (aHR 1.08, 95% CI 1.02 to 1.14, p = 0.011), and influenza A (aHR 7.10, 95% CI 1.37 to 36.8, p = 0.020) compared with influenza B infection. Conclusions: Among critically ill influenza patients, antiviral therapy was independently associated with survival. CRRT, higher severity of illness, and influenza A infection were associated with mortality.
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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 | 0.000 | 0.001 |
| 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".