Establishing an in vivo large animal model of one-lung ventilation and operative lung trauma
Bibliographic record
Abstract
BACKGROUND: Respiratory complications, including acute lung injury (ALI) and acute respiratory distress syndrome (ARDS), are important causes of morbidity and mortality among lung surgery patients. Lung surgery introduces surgical and atelectatic trauma to the operated lung, while one-lung ventilation (OLV) applied to the contralateral lung is also a suspected mechanism of ventilator-induced lung injury (VILI). Our goal was to develop a large animal model to assess the relative lung injury induced by surgical and ventilator trauma during left upper lobectomy in juvenile pigs. METHODS: Sixteen pigs (24-32 kg) were randomly assigned to one of three OLV exposure groups. The control group (n = 5) was exposed to lung-protective ventilation (LPV) during OLV, the second group (n = 5) was exposed to potentially injurious ventilation (IMV) during OLV using higher tidal volume and peak airway pressure and the third group (n = 6) was exposed to hyperoxia with protective ventilation (LPV-HO) for the duration of OLV and surgery. FINDINGS: We describe the surgical and ventilation methods for a successful lung surgery pilot for a porcine OLV model. Initial surgeries show that our protocol is effective in reproducibly maintaining peak airway pressures, tidal volumes and oxygen delivery according to the parameters of LPV, IMV and hyperoxia during OLV. Bronchoalveolar lavage fluid IL-6 was elevated in response to IMV during OLV, hyperoxia and surgical exposure. CONCLUSIONS: We describe a reproducible protocol for an in vivo large animal model of OLV lung surgery with a protective and two injurious mechanical ventilation arms with collection of physiologic data and biospecimens.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".