MétaCan
Menu
Back to cohort
Record W2335494869 · doi:10.1177/1040638716630768

Pathology of ear hematomas in swine

2016· article· en· W2335494869 on OpenAlexaff
Richard Drolet, Pierre Hélie, Sylvie D’Allaire

Bibliographic record

VenueJournal of Veterinary Diagnostic Investigation · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAutopsyGross examinationMedicineCartilageHematomaPathologyPathogenesisAnatomySurgery

Abstract

fetched live from OpenAlex

The objectives of our study were to describe the pathology of ear hematomas in swine and to add to the comprehension of the pathogenesis of this condition. The pathogenesis of aural hematomas has been studied mainly in dogs; however, disagreements exist about the precise anatomic location of the hemorrhage. Sixteen pigs with ear hematoma at various stages of development were included in this study. The pigs were submitted for routine autopsy for various and unrelated reasons over a period of several years. Based on gross examination, the 16 cases of aural hematomas were subjectively classified as acute (n = 6), subacute (n = 3), and chronic (n = 7). The age of the animals at the time of autopsy ranged from 2 weeks to adulthood, with all acute cases being <7 weeks of age. Morphologic examination of all acute cases revealed that the hematoma developed predominantly in a subperichondral location on both sides of the cartilaginous plate simultaneously. Within these same cases, there were also some areas in which blood-filled clefts had formed within the cartilage itself. Besides fibroplasia, neoformation of cartilage was found to represent a significant part of the repair process. All chronic cases were characterized on cross-section of the ear by the presence of at least 2 distinct, wavy, focally folded, and roughly parallel plates of cartilage separated from each other by fibrous tissue.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.029
GPT teacher head0.288
Teacher spread0.259 · 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 designBench or experimental
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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Veterinary Diagnostic InvestigationSame topicMicrobial infections and disease researchFrench-language works237,207