Prediction of extubation readiness in extreme preterm infants based on measures of cardiorespiratory variability
Bibliographic record
Abstract
The majority of extreme preterm infants require endotracheal intubation and mechanical ventilation (ETT-MV) during the first days of life to survive. Unfortunately this therapy is associated with adverse clinical outcomes and consequently, it is desirable to remove ETT-MV as quickly as possible. However, about 25% of extubated infants will fail and require re-intubation which is also associated with a 5-fold increase in mortality and a longer stay in the intensive care unit. Therefore, the ultimate goal is to determine the optimal time for extubation that will minimize the duration of MV and maximize the chances of success. This paper presents a new objective predictor to assist clinicians in making this decision. The predictor uses a modern machine learning method (Support Vector Machines) to determine the combination of measures of cardiorespiratory variability, computed automatically, that best predicts extubation readiness. Our results demonstrate that this predictor accurately classified infants who would fail extubation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| 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".