Effect of hydrocortisone on mortality in patients with severe community-acquired pneumonia
Bibliographic record
Abstract
PURPOSE: To determine whether hydrocortisone improves mortality in severe community-acquired pneumonia (CAP). METHODS: In an international adaptive randomized controlled platform trial testing multiple interventions, adults admitted to the intensive care unit (ICU) with severe CAP were randomized to a 7-day course of intravenous hydrocortisone (50 mg every 6 h) or control (no corticosteroid). The primary end point was 90-day all-cause mortality, analyzed iteratively by a Bayesian hierarchical model estimating distinct treatment effects for patients presenting with influenza (Y/N) and shock (Y/N). RESULTS: Fixed 7-day course hydrocortisone enrollment was stopped for futility (< 5% probability of > 20% relative improvement). Of 658 patients enrolled, 536 were randomized to hydrocortisone and 122 to control. Vital status at day 90 was missing for 15 patients. Day 90 mortality was 15% (78/521) and 9.8% (12/122) for the hydrocortisone and control groups. The adjusted odds ratio ranged from 1.52 to 1.63 (with all 95% CrI crossing 1), while the probability of > 20% relative reduction of day 90 mortality ranged from 7.1 to 3.3% across influenza and shock strata. Results were consistent in sensitivity and pre-specified secondary outcomes. In exploratory analyses, the duration of shock appeared lower in the hydrocortisone group compared with control (median (IQR) of 2 (2-5) days compared to control 3 (2-6.75) days, p value = 0.05). CONCLUSIONS: Among patients with severe CAP, treatment with a 7-day course of hydrocortisone, compared with no hydrocortisone, appears unlikely to yield a large reduction in mortality. Smaller benefits and possible harm are not excluded. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT02735707 (registration date: November 4th, 2016).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".