Modeling the Weaning of Intensive Care Unit Patients from Mechanical Ventilation: A Review
Bibliographic record
Abstract
In the intensive care unit, mechanical ventilation is a life-saving procedure, and as many as 90% of patients require the intervention. For a mechanically ventilated patient, the principal goal of a health care team is to free the patient from mechanical ventilation through weaning as soon as possible. Weaning, however, still is mostly a manual process. To achieve quick and efficient weaning, the process is needs to be automated. The first step toward automating the weaning process is building a precise model of it. The path to achieving this precision in weaning modeling, if at all possible, is laden with challenges such as the use of imprecise terms, lack of evidence, complexities in data representation as well as process specification, and uncertainty in data values as well as their implication in process evaluation. This eventually leads to a lack of universally accepted and followed standards and guidelines. Despite the magnitude of these challenges, various weaning automations have been attempted through mathematical modeling or knowledge-based modeling. Some of these have been available as commercial mechanical ventilator modes since the 1990s. Even though much potential has been demonstrated through clinical trials, their infrequent usage indicates a lack of consensus concerning their applicability.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".