Pressure–Time Curve Predicts Minimally Injurious Ventilatory Strategy in an Isolated Rat Lung Model
Bibliographic record
Abstract
BACKGROUND: We tested the hypothesis that the pressure-time (P-t) curve during constant flow ventilation can be used to set a noninjurious ventilatory strategy. METHODS: In an isolated, nonperfused, lavaged model of acute lung injury, tidal volume and positive end-expiratory pressure were set to obtain: (1) a straight P-t curve (constant compliance, minimal stress); (2) a downward concavity in the P-t curve (increasing compliance, low volume stress); and (3) an upward concavity in the P-t curve (decreasing compliance, high volume stress). The P-t curve was fitted to: P = a. tb +c, where b describes the shape of the curve, b = 1 describes a straight P-t curve, b < 1 describes a downward concavity, and b > 1 describes an upward concavity. After 3 h, lungs were analyzed for histologic evidence of pulmonary damage and lavage concentration of inflammatory mediators. Ventilator-induced lung injury occurred when injury score and cytokine concentrations in the ventilated lungs were higher than those in 10 isolated lavaged rats kept statically inflated for 3 h with an airway pressure of 4 cm H2O. RESULTS: The threshold value for coefficient b that discriminated best between lungs with and without histologic and inflammatory evidence of ventilator-induced lung injury (receiver-operating characteristic curve) ranged between 0.90-1.10. For such threshold values, the sensitivity of coefficient b to identify noninjurious ventilatory strategy was 1.00. A significant relation (P < 0.001) between values of coefficient b and injury score, interleukin-6, and macrophage inflammatory protein-2 was found. CONCLUSIONS: The predictive power of coefficient b to predict noninjurious ventilatory strategy in a model of acute lung injury is high.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".