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Record W4404676021 · doi:10.31533/pubvet.v18n12e1702

Pneumonia aspirativa em cão

2024· article· en· W4404676021 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePubVet · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsnot available
Fundersnot available
KeywordsPneumoniaMedicineInternal medicine

Abstract

fetched live from OpenAlex

Aspiration pneumonia is a pathology resulting from the inhalation of liquid or solid contents via the airways, which leads to inflammation in the lung parenchyma. The course of the disease varies according to the amount and type of material aspirated, as well as the animal's general state of health. The symptoms of the disease are usually acute and severe. In this sense, the drug therapy chosen for treatment depends on the degree to which the lung is compromised and the clinical signs. The surgical procedure of total or partial lobectomy can be advantageous when the lung lesions are irreversible and clinical therapy has been unsuccessful. The aim of this paper is to report the case of aspiration pneumonia in a female Labrador retriever who presented pulmonary crackles after drowning and was given a pulmonary lobectomy. Computed tomography was used to confirm the diagnosis of aspiration pneumonia. This highlights the importance of carrying out complementary tests in order to offer the animal a better therapeutic approach.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0060.011

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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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