A Method for Noninvasive Determination of Inspiratory Resistance during Proportional Assist Ventilation
Bibliographic record
Abstract
Currently available noninvasive methods for measuring inspiratory resistance (RI) are difficult to implement or interpret during assisted mechanical ventilation on account of the confounding effect of respiratory efforts (Pmus). We propose a simple method consisting of brief reductions in airway pressure (Paw) in the early part of the inflation phase (pulse). Paw, flow (V), and volume (V) are measured at the beginning of the pulse (T (0)), at the trough of the pulse (TI) and at a point 0.1 s before T(0) (T(-1)). Equations of motion of the form [Pmus + Paw = V. K(1) + V (2). K(2) +V. E] are generated for the data at the three time points (E = elastance, K(1) and K(2) are Rohrer's constants). These three equations can be solved for K(1) and K(2) if it is arranged that the pulse has appropriate configuration and timing, and if it is assumed that DeltaPmus/Deltat is constant over the brief pulse period. The method was tested in 67 patients ventilated with proportional assist ventilation (PAV). The results were compared with those obtained using the interrupter technique during a period of controlled mechanical ventilation (CMV). RI, expressed at a standard flow of 1 L. s(-)(1), was slightly higher during PAV (16.4 +/- 4.9 versus 15.5 +/- 4.5 cm H(2)O. L(-1). s, p < 0.001). The average difference was 0.9 +/- 2.0 cm H(2)O. L(-1). s, corresponding to 5.4 +/- 12.6% of the average of RCMV and RPAV. The correlation coefficient was 0.92 (p = 8E-28) with a slope (1.01) and intercept (0.8) not significantly different from 1.0 and 0, respectively. We conclude that brief negative pulses applied early during the inflation phase can be used to provide reliable estimates of inspiratory resistance during PAV.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".