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Record W2069988938 · doi:10.1155/2012/926803

Use of an Endobronchial Blocker and Selective Lung Ventilation to Aid Surgical Removal of a Lung Lobe Abscess in a Dog

2012· article· en· W2069988938 on OpenAlexaboutno aff
Carl Bradbrook, Louise Clark, Martina Mosing

Bibliographic record

VenueCase Reports in Veterinary Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnesthesiaThoracoscopyLungThoracotomyVentilation (architecture)AlfentanilSurgeryFentanylInternal medicine

Abstract

fetched live from OpenAlex

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 (PaO 2 : FiO 2 ) 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 (CaO 2 ) 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.357
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

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