Commentaries on Viewpoint: Looking beyond macroventilatory parameters and rethinking ventilator-induced lung injury
Bibliographic record
Abstract
Current methods of mechanical ventilator management consist of mostly macroparameters that are only reflecting the values of the lungs as a whole and not considerate of heterogeneity of the microenvironment (2).Kollisch-Singule et al. ( 2) suggested the microenvironment of the lungs will undergo mechanical stress due to an unevenly distributed tidal volume during ventilation, resulting in lung injury, additional inflammation, and possible rupturing of alveolar cell membranes.We agree that the aforementioned side effects are a serious call for more research needing to be done in the field of mechanical ventilation techniques.The statement that there is no optimal positive end expiratory pressure (PEEP) for mechanical ventilation, as one of the potential macroparameters responsible for possible injuries, needs to be further explored (2).Setting the PEEP above the lower inflection point may have merit in preventing injuries in addition to keeping superimposed airway pressure below applied airway pressure to prevent potential collapses of lung units (1, 3).Sundersan et al. ( 4) created a new model-based recruitment tool that would help in determining an "optimal" PEEP by evaluating threshold opening pressure (TOP) and threshold closing pressure (TCP) distributed for each part of breathing cycles at a given PEEP.A standard deviation and mean value for TOP and TCP can be derived from the given information and the distributions uniquely depicted the patient's condition and state of their diseases (4, 5).This is a potential noteworthy way to individualize each patients' PEEP, and future studies should look more into recruitment models.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.058 | 0.056 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".