Effect of Ventilatory Variability on Occurrence of Central Apneas
Bibliographic record
Abstract
OBJECTIVE: To compare the influence of 2 ventilation strategies on the occurrence of central apneas. METHODS: This was a prospective, comparative, crossover study with 14 unsedated subjects undergoing weaning from mechanical ventilation in the medical ICU of Hôpital du Sacré-Cœur, Montréal, Québec, Canada. The subjects were ventilated alternately in neurally adjusted ventilatory assist (NAVA) and pressure support ventilation (PSV) modes. Inspiratory flow/time and pressure/time waveforms and diaphragmatic electrical activity were used to detect central apneas. Ventilatory variability and breathing pattern were evaluated in both modes. Breathing patterns just before central apneas, and associations between apneas and sleep patterns (electroencephalogram) were studied. RESULTS: Switching from PSV to NAVA did not change mean minute ventilation, tidal volume, or breathing frequency. However, tidal volume variability, defined as the coefficient of variability (standard error/mean), was significantly greater with NAVA than with PSV (17.2 ± 8 vs 10.3 ± 4, P = .045). NAVA induced a greater decrease in central apneas, compared to PSV (to 0 with NAVA vs 10.5 ± 11 with PSV, P = .005). Central apneas during PSV were detected only during non-rapid-eye-movement sleep. CONCLUSIONS: NAVA was associated with increased ventilatory variability, compared to constant-level PSV. With NAVA the absence of over-assistance during sleep coincided with absence of central apneas, suggesting that load capacity and/or neuromechanical coupling were improved by NAVA and that this improvement decreased or abolished central apneas.
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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 | 0.001 | 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".