Variability of End-Expiratory Lung Volume in Premature Infants
Bibliographic record
Abstract
BACKGROUND: Analysis of breath-to-breath variability of respiratory characteristics provides information on the respiratory control. In infants, the control of end-expiratory lung volume (EELV) is active and complex, and it can be altered by respiratory disease. The pattern of EELV variability may reflect the behavior of this regulatory system. OBJECTIVES: We aimed to characterize EELV variability in premature infants, and to evaluate variability pattern changes associated with respiratory distress and ventilatory support. METHODS: EELV variations were recorded using inductance plethysmography in 18 infants (gestational age 30-33 weeks) during the first 10 days of life. An autocorrelation analysis was conducted to evaluate the 'EELV memory', i.e. the impact of the characteristics of one breath on the following breaths. RESULTS: In infants without respiratory symptoms, EELV variability was high, with large standard deviations of EELV. Autocorrelation was found to be significant until a median lag of 7 (interquartiles: 4-8) breaths. Autocorrelation was markedly prolonged in patients with respiratory distress or ventilatory support, with a higher number of breath lags with significant autocorrelation (p < 0.01) and higher autocorrelation coefficients (p < 0.05). Conventional assisted ventilation does not re-establish a healthy EELV profile and is associated with lower respiratory variability. CONCLUSIONS: In premature infants, EELV variability pattern is modified by respiratory distress with a prolonged 'EELV memory', which suggests a greater instability of the control of EELV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| 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".