F <scp>ifty</scp> Y <scp>ears</scp> <scp>of</scp> R <scp>esearch</scp> <scp>in</scp> ARDS. Setting Positive End-Expiratory Pressure in Acute Respiratory Distress Syndrome
Bibliographic record
Abstract
Abstract Positive end-expiratory pressure (PEEP) has been used during mechanical ventilation since the first description of acute respiratory distress syndrome (ARDS). In the subsequent decades, many different strategies for optimally titrating PEEP have been proposed. Higher PEEP can improve arterial oxygenation, reduce tidal lung stress and strain, and promote more homogenous ventilation by preventing alveolar collapse at end expiration. However, PEEP may also cause circulatory depression and contribute to ventilator-induced lung injury through alveolar overdistention. The overall effect of PEEP is primarily related to the balance between the number of alveoli that are recruited to participate in ventilation and the amount of lung that is overdistended when PEEP is applied. Techniques to assess lung recruitment from PEEP may help to direct safer and more effective PEEP titration. Some PEEP titration strategies attempt to weigh beneficial effects on arterial oxygenation and on prevention of cyclic alveolar collapse with the harmful potential of overdistention. One method for PEEP titration is a PEEP/FiO2 table that prioritizes support for arterial oxygenation. Other methods set PEEP based on mechanical parameters, such as the plateau pressure, respiratory system compliance, or transpulmonary pressure. No single method of PEEP titration has been shown to improve clinical outcomes compared with other approaches of setting PEEP. Future trials should focus on identifying individuals who respond to higher PEEP with recruitment and on clinically important outcomes (e.g., mortality).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.010 |
| 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; both teacher heads agree on what is shown here.
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".