Heterogeneous impact of Sighs on mortality in patients with acute hypoxemic respiratory failure: insights from the PROTECTION study
Bibliographic record
Abstract
Abstract Background Sigh breaths may impact outcomes in acute hypoxemic respiratory failure (AHRF) during assisted mechanical ventilation. We investigated whether sigh breaths may impact mortality in predefined subgroups of patients enrolled in the PROTECTION multicenter clinical trial according to: 1.the physiological response in oxygenation to Sigh (responders versus non-responders) and 2.the set levels of positive end-expiratory pressure (PEEP) (High vs. Low-PEEP). If mortality differed between Sigh and No Sigh, we explored physiological daily differences at 7-days. Results Patients were randomized to pressure support ventilation (PSV) with Sigh (Sigh group) versus PSV with no sigh (No Sigh group). (1) Sighs were not associated with differences in 28-day mortality in responders to baseline sigh-test. Contrarily-in non-responders-56 patients were randomized to Sigh (55%) and 28-day mortality was lower with sighs (17%vs.36%, log-rank p = 0.031). (2) In patients with PEEP > 8cmH 2 O no difference in mortality was observed with sighs. With Low-PEEP, 54 patients were randomized to Sigh (48%). Mortality at 28-day was reduced in patients randomised to sighs (13%vs.31%, log-rank p = 0.021). These findings were robust to multivariable adjustments. Tidal volume, respiratory rate and ventilatory ratio decreased with Sigh as compared with No Sigh at 7-days. Ventilatory ratio was associated with mortality and successful extubation in both non-responders and Low-PEEP. Conclusions Addition of Sigh to PSV could reduce mortality in AHRF non-responder to Sigh and exposed to Low-PEEP. Results in non-responders were not expected. Findings in the low PEEP group may indicate that insufficient PEEP was used or that Low PEEP may be used with Sigh. Sigh may reduce mortality by decreasing physiologic dead space and ventilation intensity and/or optimizing ventilation/perfusion mismatch. Clinical Trial Registration : ClinicalTrials.gov; Identifier: NCT03201263.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".