Prediction of successful veno‐venous extracorporeal life support liberation using the oxygen challenge test
Bibliographic record
Abstract
Abstract Background The oxygen challenge test (OCT) is an underutilized measure of lung recovery, easily performed prior to proceeding with a trial‐off V‐V ECLS as part of a weaning algorithm. Evidence‐based thresholds for OCT results which support continuing with V‐V ECLS weaning are lacking, making interpretation of these tests challenging in clinical practice. Methods We performed a retrospective review of patients commenced on V‐V ECLS as a bridge‐to‐recovery at Vancouver General Hospital from 2015–2019. The absolute PaO2 post‐OCT and change in PaO2 proportional to incremental FiO2 change on the ventilator (∆PaO2) were evaluated as predictive screening metrics for identifying conditions favorable for successful trial‐off of V‐V ECLS. Results An optimal cut‐off of PaO2 ≥ 240 mm Hg post‐OCT (AUC 0.77) and ∆PaO2 ≥ 250 mm Hg (AUC 0.76) was identified as a threshold for predicting successful trials‐off. A total of 26 and 24 patients achieved post‐OCT PaO2 and ∆PaO2 thresholds, and 100% of these patients were liberated successfully from ECLS during their admission. Conclusions The OCT can serve as an effective screen of shunt reduction and native lung recovery which can be used alongside other measures of ventilation to assess for suitability of liberation from V‐V ECLS prior to a trial‐off. Achieving a PaO2 ≥ 240 mm Hg post‐OCT is a strong prognostic indicator for successful liberation from V‐V ECLS during ICU admission.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".