Heart Rate Variability and Extubation Readiness in Extremely Preterm Infants
Bibliographic record
Abstract
BACKGROUND: Mechanical ventilation (MV) is associated with changes in autonomic nervous system activity in preterm infants, which can be assessed by measurements of heart rate variability (HRV). Decreased HRV has been described in adults undergoing disconnection from MV; such information is not available in preterm infants. OBJECTIVE: To compare differences in HRV between infants successfully extubated and those who failed, and to evaluate the accuracy of HRV as a predictor of extubation readiness. METHODS: This is a prospective, observational study of infants with a birth weight ≤1,250 g undergoing their first extubation attempt. Heart rate was measured during a 60-min period immediately prior to extubation and HRV was calculated using the frequency domain analysis. RESULTS: A total of 47 infants were studied; 36 were successfully extubated and 11 reintubated. There were no differences in patient demographics, ventilator settings, blood gases or postextubation management between the groups. All components of the HRV analysis were significantly decreased in infants who failed, generating high areas under the receiver operating characteristic curve. The specificity and positive predictive values were 100, but with limited sensitivity and negative predictive values. CONCLUSIONS: Infants considered 'ready to be extubated' but who subsequently failed their first extubation attempt had decreased HRV prior to extubation. Though promising, the value of HRV as a predictor of extubation readiness requires further evaluation.
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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 | 0.000 | 0.004 |
| 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) | 0.001 | 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".