Liberation from Mechanical Ventilation: Established and New Insights
Bibliographic record
Abstract
A substantial proportion of critically ill patients require ventilator support with the majority requiring invasive mechanical ventilation. Timely and safe liberation from invasive mechanical ventilation is a critical aspect of patient care in the intensive care unit (ICU) and is a top research priority for patients and clinicians. In this article, we discuss how to (1) identify candidates for liberation from mechanical ventilation, (2) conduct spontaneous breathing trials (SBTs), and (3) optimize patients for liberation from mechanical ventilation. We also discuss the roles for (4) extubation to noninvasive ventilation and (5) newer modes of mechanical ventilation during liberation from mechanical ventilation. We conclude that, though substantial progress has been made in identifying patients who are likely to be liberated (e.g., through the use of SBTs) and management strategies that speed liberation from the ventilator (e.g., protocolized SBTs, lighter sedation, and early mobilization), many important questions regarding liberation from mechanical ventilation in clinical practice remain unanswered.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".