Predictors of VILI risk: driving pressure, 4DPRR and mechanical power ratio—an experimental study
Bibliographic record
Abstract
Abstract Background Ventilator-induced lung injury (VILI) is one of the side effects of mechanical ventilation during ARDS; a prerequisite for averting it is the quantification of its risk factors associated with a given ventilatory setting. Many clinical variables have been proposed as predictors of VILI, of which driving pressure is the most widely used. In this study, we compared the performance of driving pressure, four times the driving pressure added to respiratory rate (4DPRR) and mechanical power ratio. Results In a study population of 121 previously healthy pigs exposed to harmful ventilation, we compared the association of driving pressure, 4DPRR and mechanical power ratio to lung weight, lung wet-to-dry and total histological score. All the three variables were associated with these outcomes. Driving pressure, 4DPRR and mechanical power ratio increase linearly with the lung weight (adjusted R 2 of 0.27, 0.36 and 0.40, respectively), the lung wet-to-dry ratio (adjusted R 2 of 0.19, 0.25 and 0.37) and the total histological score (adjusted R 2 of 0.26, 0.38 and 0.26). Using a multiple linear regression model with forward analysis, starting with tidal volume and progressively adding respiratory rate and positive end-expiratory pressure, and comparing the topic with the outcome variables, we obtained R 2 values, respectively, of 0.07, 0.20, 0.42 for lung weight, 0.09, 0.19, 0.26 for lung wet-to-dry ratio and 0.07, 0.27, 0.43 for total histological score. Conclusions Driving pressure, 4DPRR and mechanical power ratio, were all associated with lung injury in healthy animals undergoing mechanical ventilation.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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; 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".