Optimum positive end-expiratory pressure 40 years later
Bibliographic record
Abstract
In 1975, Suter and colleagues published a fascinating study trying to determine the “optimum” level of end-expiratory pressure (PEEP) in patients with acute respiratory failure receiving mechanical ventilation.[1] The fascinating aspect of the paper was that they proposed a physiological model of the acute respiratory distress syndrome (ARDS) based on the relationship between lung volume, respiratory mechanics (compliance), dead space, cardiac output, and shunt and oxygenation. This model is still very useful today. Some of the messages of this paper should not be forgotten: Although increasing PEEP seemed to be beneficial, it had a double effect and above a certain value, PEEP was probably more risky than beneficial, with increase in dead space and reduction in cardiac output and oxygen delivery. The message about oxygen delivery was important, stressing that what really mattered was not the level of PaO2 in the blood but the quantity of oxygen carried by hemoglobin, as measured by oxygen transport. Therefore, increasing PaO2 at the expense of a decrease in cardiac output would result in a net negative effect in terms of oxygen delivered to the tissue. If the goal of PEEP is to increase oxygenation, this makes a lot of sense and continuing to look at PaO2 without considering cardiac output is conceptually a major limitation.[2] In addition, a positive relationship between shunt, i.e. the percentage of cardiac output passing through non-ventilated areas, and cardiac output was demonstrated.[3] Dantzker, a few years later, even suggested that an important reason for the observed improvement in oxygenation with PEEP in ARDS was the reduction in cardiac output and the shunt-cardiac output relationship.[4] It was also demonstrated later, however, that maintaining cardiac output with inotropic agents had significant benefits of PEEP on oxygenation.[5]
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".