Biologically Variable or Naturally Noisy Mechanical Ventilation Recruits Atelectatic Lung
Bibliographic record
Abstract
Biologically variable mechanical ventilation (Vbv)-using a computer-controller to mimic the normal variability in spontaneous breathing-improves gas exchange in a model of severe lung injury (Lefevre, G. R., S. E. Kowalski, L. G. Girling, D. B. Thiessen, W. A. C. Mutch. Am. J. Respir. Crit. Care Med. 1996;154:1567-1572). Improved oxygenation with Vbv, in the face of alveolar collapse, is thought to be due to net volume recruitment secondary to the variability or increased noise in the peak inspiratory airway pressures (Ppaw). Biologically variable noise can be modeled as an inverse power law frequency distribution (y approximately 1/f(a)) (West, B. J., M. Shlesinger. Am. Sci. 1990;78:40-45). In a porcine model of atelectasis-right lung collapse with one-lung ventilation-we studied if Vbv (n = 7) better reinflates the collapsed lung compared with conventional monotonously regular control mode ventilation (Vc; n = 7) over a 5-h period. We also investigated the influence of sigh breaths with Vc (Vs; n = 8) with this model. Reinflation of the collapsed lung was significantly enhanced with Vbv-greater Pa(O(2)) (502 +/- 40 mm Hg with Vbv versus 381 +/- 40 mm Hg with Vc at 5 h; and 309 +/- 79 mm Hg with Vs; mean +/- SD), lower Pa(CO(2)) (35 +/- 4 mm Hg versus 48 +/- 8 mm Hg and 50 +/- 8 mm Hg), lower shunt fraction (9.7 +/- 2.7% versus 14.6 +/- 2.0% and 22.9 +/- 6.0%), and higher respiratory system compliance (Crs) (1.15 +/- 0.15 ml/cm H(2)O/kg versus 0.79 +/- 0.19 ml/cm H(2)O/kg and 0.77 +/- 0.13 ml/cm H(2)O/kg)-at lower mean Ppaw (15.7 +/- 1.4 cm H(2)O versus 18.8 +/- 2.3 cm H(2)O and 18.9 +/- 2.8 cm H(2)O). Vbv resulted in an 11% increase in measured tidal volume (VT(m)) over that seen with Vc by 5 h (14.7 +/- 1.2 ml/kg versus 13. 2 ml/kg). The respiratory rate variability programmed for Vbv demonstrated an inverse power law frequency distribution ( y approximately 1/f(a)) with a = 1.6 +/- 0.3. These findings provide strong support for the theoretical model of noisy end-inspiratory pressure better recruiting atelectatic lung. Our results suggest that using natural biologically variable noise has enhanced the performance of a mechanical ventilator in control mode.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".