Cardiorespiratory signal analysis in extremely preterm infants during the peri-extubation period
Bibliographic record
Abstract
Background: Mechanical ventilation (MV) is common among extremely preterm (EPT) infants and prediction/prevention of extubation failure remains an enigma.One controversial approach is the use of an endotracheal continuous positive airway pressure (ETT-CPAP) trial while observing stability of vital signs.After extubation, infants are treated with some type of noninvasive respiratory support: nasal CPAP (NCPAP), high flow nasal cannula (HFNC), nasal intermittent positive pressure ventilation (NIPPV), and non-invasive neurally adjusted ventilatory assist (NIV-NAVA).Despite many trials, due to significant methodological issues, the superiority of a particular type is not entirely clear, although there are some encouraging findings with patient-synchronized ventilation through NIPPV and NIV-NAVA.In the neonatal intensive care unit (NICU) vital signs are continuously monitored and these signals are available for analyses of cardiorespiratory behavior.Investigations include analysis of heart rate variability (HRV), diaphragmatic activity, and respiratory variability (RV).While these measures are typically used in research settings, there is growing evidence for its clinical usefulness in providing more precise and individualized information. Objectives:The overall aim of this thesis is to investigate cardiorespiratory behavior of EPT infants during the peri-extubation period.The specific objectives are to evaluate the effects of an ETT-CPAP trial prior to extubation, and different types of non-invasive respiratory support provided after extubation on the cardiorespiratory behavior.The utility of cardiorespiratory behavior measures for extubation outcomes was also investigated.Methods: A series of prospective crossover studies were designed and performed.All studies were done in the Montreal Children's Hospital NICU and included EPT infants with birth weight 3.1
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 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".