MétaCan
Menu
Back to cohort
Record W3112300100 · doi:10.2460/javma.258.1.85

Lateralization of the diaphragm for thoracic wall reconstruction in a dog

2020· article· en· W3112300100 on OpenAlexaboutno aff
Oliver Gilman, Daniel Ogden

Bibliographic record

VenueJournal of the American Veterinary Medical Association · 2020
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsDiaphragm (acoustics)Lateralization of brain functionAnatomyThoracic wallMedicineEngineeringAudiologyElectrical engineering

Abstract

fetched live from OpenAlex

CASE DESCRIPTION: A 7-year-old 35-kg (77-lb) neutered male Labrador Retriever was evaluated because of a 1-month history of a rapidly growing mass associated with the right caudal aspect of the thoracic wall. CLINICAL FINDINGS: CT examination revealed an aggressive, osteolytic mass lesion centered around the ventral aspect of the right ninth rib with osteolysis of that rib and focal invasion into the right external abdominal oblique muscle. Preoperative cytologic and histologic findings were most consistent with a chondrosarcoma. TREATMENT AND OUTCOME: The mass and the eighth, ninth, and tenth ribs were resected, and thoracic wall reconstruction was performed with a novel surgical technique involving lateralization of the diaphragm. The dog recovered rapidly and without complications other than a small seroma; no paradoxical chest movement developed, and the cosmetic outcome was good. An excellent quality of life was reported after surgery until the dog was euthanized because of underlying disease progression 6 months later. CLINICAL RELEVANCE: Diaphragmatic lateralization was a simple method of caudal thoracic wall reconstruction that had good clinical results in this case. Research is needed to further assess the safety, reliability, and potential complications of this procedure in dogs.

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)0.003
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 score0.141
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.054
GPT teacher head0.390
Teacher spread0.337 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Veterinary Medical AssociationSame topicVeterinary Oncology ResearchFrench-language works237,207