Effects of combined one-lung ventilation and intrathoracic carbon dioxide insufflation on intrathoracic working space when performing thoracoscopy in dogs
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of combining one-lung ventilation and carbon dioxide insufflation (OLV-CDI) on intrathoracic working space (determined by means of CT) during thoracoscopy in dogs and investigate conditions that could safely improve working space compared with OLV alone. ANIMALS: 6 healthy Beagles. PROCEDURES: Dogs were anesthetized, and right- or left-sided (n = 3/side) OLV was instituted. On the blocked side, a laparoscopic trocar sleeve was placed in the ninth intercostal space for CDI. CT was performed under 3 conditions: with OLV alone, with OLV-CDI at an intrapleural pressure (IPP) of 3 mm Hg, and with OLV-CDI at an IPP of 5 mm Hg. Working space volume (WSV), ventilation space volume (VSV), and thoracic cavity volume (TCV) were determined from CT images. RESULTS: With OLV-CDI at an IPP of 3 or 5 mm Hg, WSV and TCV were significantly increased, compared with values obtained during OLV alone. With OLV-CDI at an IPP of 5 mm Hg, VSV and Spo2 were significantly decreased, compared with values obtained during OLV alone. Additionally, contralateral pneumothorax was observed in 4 dogs at an IPP of 5 mm Hg. CLINICAL RELEVANCE: Combining OLV and CDI could provide a larger working space than OLV alone, even with an IPP of 3 mm Hg, in dogs of limited size. However, an evaluation of the effects on oxygenation and cardiovascular variables is needed before clinical use.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".