Noninvasive ventilation in critically ill patients with the Middle East respiratory syndrome
Bibliographic record
Abstract
BACKGROUND: Noninvasive ventilation (NIV) has been used in patients with the Middle East respiratory syndrome (MERS) with acute hypoxemic respiratory failure, but the effectiveness of this approach has not been studied. METHODS: Patients with MERS from 14 Saudi Arabian centers were included in this analysis. Patients who were initially managed with NIV were compared to patients who were managed only with invasive mechanical ventilation (invasive MV). RESULTS: Of 302 MERS critically ill patients, NIV was used initially in 105 (35%) patients, whereas 197 (65%) patients were only managed with invasive MV. Patients who were managed with NIV initially had lower baseline SOFA score and less extensive infiltrates on chest radiograph compared with patients managed with invasive MV. The vast majority (92.4%) of patients who were managed initially with NIV required intubation and invasive mechanical ventilation, and were more likely to require inhaled nitric oxide compared to those who were managed initially with invasive MV. ICU and hospital length of stay were similar between NIV patients and invasive MV patients. The use of NIV was not independently associated with 90-day mortality (propensity score-adjusted odds ratio 0.61, 95% CI [0.23, 1.60] P = 0.27). CONCLUSIONS: In patients with MERS and acute hypoxemic respiratory failure, NIV failure was very high. The use of NIV was not associated with improved outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".