Pressure Increase Due to Hydrostatic Pressure of Perfluorocarbon
Bibliographic record
Abstract
With great interest we read the recent article by Kacmarek and colleagues (1). In the trial reported therein, the authors conclude that partial liquid ventilation (PLV) results in a greater number of serious adverse effects and does not improve outcome compared with conventional ventilation in patients with severe ARDS. As one of the reasons for a higher complication rate, the authors suggest the high peak alveolar pressure due to the additional hydrostatic pressure exerted by the perfluorocarbon (PFC), which has to be added to the measured airway pressure. It is obvious that a measurement of the hydrostatic pressure is associated with technical difficulties, though it is possible to estimate it. In this study the PFC was instilled to the carina in the supine position. Therefore, the maximal hydrostatic pressure (cm H2O) can be calculated by multiplying the distance between the liquid surface and the deepest point in the thorax, the dorsovisceral pleura (in cm), and the specific density of the PFC, which is 1.92. To evaluate the effective additional hydrostatic pressure, we measured 40 patients, ages between 18 and 60 yr, median 44.6 12.8 yr, 60% male sex, who had a thoracic CT in the supine position. On the Diagnostic Workstation (Diagnostic DICOM 3.0 Workstation; Agfa-Gevaert Group, Morstel, Belgium), the distance between the level of the carina and the lowest point of the dorsovisceral pleura was measured with the on-screen tool. The median distance retrieved was 7.5 cm 1.2 cm. Using these findings for estimating the actual resulting airway pressure, the distance was multiplied by 1.92. Therefore in the “low-dose” group, the hydrostatic pressure of 14.4 2.36 cm H2O has to be added to the measured plateau pressure as well as to the measured PEEP. Following this estimation, the median plateau pressure would be as high as 45.8 cm H2O with a PEEP of 28.7 cm H2O. These excessively high pressures could explain the higher rate of adverse effects in the “low-dose” group. For the “high-dose” group the resulting pressure would even exceed these findings. As is known from the literature (2), increased airway pressures lead to a worse outcome. Therefore, to avoid barotrauma to the lung during PLV, which probably contributes to a worse outcome, the measured pressure levels during PLV should have been significantly reduced. Therefore, the conclusion based on the findings of this study should be reconsidered.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".