Late Breaking Abstract - Effects of Spontaneous Breathing, Positive Airway Pressure, and pRoteCtive Mechanical Ventilation in a Preclinical Model of Acute Lung Injury. The SPARC Study
Bibliographic record
Abstract
Background The risk of patient self-inflicted lung injury (P-SILI) and the impact of CPAP, unassisted spontaneous breathing, and protective mechanical ventilation in ARDS remain incompletely understood. Methods In a porcine model of severe ARDS, animals were randomized to: unassisted breathing (P-SILI; PEEP 2cmH₂O, pressure support 2cmH₂O); CPAP (PEEP 10cmH₂O, pressure support 2cmH₂O); or mechanical ventilation (VCV). VCV followed protective strategies: prone positioning, driving pressure <14 cmH₂O, and PEEP titrated to end-expiratory transpulmonary pressure of 0–2cmH₂O. In P-SILI and CPAP, sedatives were titrated to muscular inspiratory pressures of 15–25cmH₂O. All animals received 100% FiO₂. Preliminary results from 12 animals (n=4/group) are reported. Results PaCO₂ was higher in P-SILI and CPAP than VCV; oxygen saturation was better in CPAP than P-SILI. VCV animals had higher PaO₂/FiO₂ (p<0.05) and better compliance (p<0.05). After injury, ventilation shifted ventrally in all groups and dorsally over time (p<0.001). At the end, all animals were ventilated identically and VCV maintained higher PaO₂/FiO₂ (p=0.03) and compliance (p=0.004)(Figure1). erj;66/suppl_69/PA1011/F1 F1 F1 Conclusions In a large animal model of ARDS, mechanical ventilation with prone positioning, low driving pressure, and individualized PEEP may increase lung protection compared to unsupported spontaneous breathing.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".