Use of an Endobronchial Blocker and Selective Lung Ventilation to Aid Surgical Removal of a Lung Lobe Abscess in a Dog
Bibliographic record
Abstract
This paper documents use of an endobronchial blocker (EBB) to achieve selective lung ventilation (SLV) for the purpose of lung lobectomy with thoracoscopy. A 3-year-old female neutered Labrador Retriever, body mass of 18.5 kg, was presented for exploratory thoracoscopy. Acepromazine and methadone were administered as premedication, and anaesthesia was induced with propofol and maintained with isoflurane in 100% oxygen and continuous infusions of fentanyl and lidocaine. Mechanical ventilation of the dog’s lungs was performed prior to placement of an Arndt EBB caudal to the right cranial bronchus to allow SLV. Successful SLV was achieved with this technique, allowing continued inflation of the right cranial lobe. A reduction in the arterial partial pressure of oxygen to fractional inspired oxygen ratio (PaO2 : FiO2) of 444 to 306 occurred after placement of the EBB, with no change in monitored cardiopulmonary variables. F-shunt increased from 17.4% to 23.7% with a reduction in oxygen content (CaO2) of 20.0 to 18.7 mg dL-1, remaining within the physiologic range. Due to lung adhesions to the diaphragm, conversion to thoracotomy was required for completion of the procedure. This technique is challenging to perform in the dog. Arterial blood gas analysis should be performed to allow adequate monitoring of ventilation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.004 | 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".