Determinants of Effect of Extracorporeal CO <sub>2</sub> Removal in Hypoxemic Respiratory Failure
Bibliographic record
Abstract
BACKGROUND: Dead space and respiratory system elastance (Ers) may influence the clinical benefit of a ventilation strategy combining very low tidal volume (VT) with extracorporeal carbon dioxide removal (ECCO2R) in patients with acute hypoxemic respiratory failure. We sought to evaluate whether the effect of ECCO2R on mortality varies according to ventilatory ratio (VR; a composite variable reflective of dead space and shunt) and Ers. METHODS: Secondary analysis of a trial of a strategy combining very low VT and low-flow ECCO2R planned before the availability of trial results. Bayesian logistic regression was used to estimate the posterior probability of effect moderation by VR, Ers, and severity of hypoxemia (ratio of arterial partial pressure of oxygen to fraction of inspired oxygen [PaO2:FiO2]) on 90-day mortality. Credibility of effect moderation was appraised according to the Instrument for Assessing the Credibility of Effect Modification Analyses criteria. RESULTS: A total of 405 patients were available for analysis. The effect of the intervention on mortality varied substantially with VR (posterior probability of interaction, 94%; high credibility). Benefit was more probable than harm in patients with VR 3 or higher. In patients with VR less than 3, the probability of increased mortality with intervention was high (>90%). The effect of the intervention also varied with PaO2:FiO2 (posterior probability of interaction, >99%; low credibility). Benefit was more probable than harm in patients with PaO2:FiO2 110 mm Hg or higher. The effect of the intervention did not vary substantially with Ers (posterior probability of interaction, 68%; low credibility). CONCLUSIONS: VR has a highly credible influence on the effect of a strategy combining very low VT and low-flow ECCO2R on mortality. This intervention may reduce mortality in patients with high VR. (Funded by an Early Career Investigator Award from the Canadian Institutes of Health Research to Dr. Goligher.)
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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 | 0.028 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".