A Method for Measuring Passive Elastance during Proportional Assist Ventilation
Bibliographic record
Abstract
There are currently no reliable, noninvasive ways to monitor respiratory elastance (E) during assisted ventilation. We describe a method that is suited for proportional assist ventilation (PAV). In this mode, the end of the ventilator's inflation phase occurs during the declining phase of inspiratory effort (Pmus). If the opening of the exhalation valve is delayed, airway pressure (Paw) should initially rise as Pmus continues its decline. When Pmus declines to zero, a Paw plateau should appear. Paw at this point should reflect passive recoil at the prevailing volume. A cohort of 74 ventilator-dependent patients, ventilated in the PAV mode, were studied. Brief end-inspiratory occlusions were applied at random intervals. The magnitude of early change in Paw during the occlusion was inversely related to level of assist (r = 0.7, p < 0.00001). At high assist (> 75%), Paw was nearly flat or declined slightly, indicating minimal residual Pmus at the onset of occlusion. At lower assist levels, Paw increased exponentially in most patients with an average time constant of 0.21 +/- 0.06 s. Extraneous events that may corrupt the measurement (e.g., behavioral responses) were extremely rare (< 0.5%) in the first 0.25 s. From these findings, we concluded that Paw measured 0.25 s from occlusion onset (P0.25) includes little inspiratory Pmus and is free of extraneous events. E, estimated from P0.25 during PAV (EPAV), agreed well (r = 0.92) with passive E measured during controlled ventilation (ECMV); the average difference (EPAV - ECMV) was (+/- SD) -0.3 +/- 4.9 cm H2O x L(-1), corresponding to 0.9 +/- 16.4% of average E. We conclude that Paw measured at 0.25 s from the onset of end-inspiratory occlusion in the PAV mode provides a reliable estimate of passive elastic recoil.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".