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Record W4289787359

Laboratory investigation of cases of fatal bacterial pneumonia in dairy cows.

2022· article· en· W4289787359 on OpenAlexaffabout
DeLenn Burrows, Ðurđa Slavić, Cynthia Miltenburg, Davor Ojkić, Andrew S. Brooks, Jeff Caswell

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsMinistry of Agriculture, Food and Rural Affairs
Fundersnot available
KeywordsPneumoniaMedicineOutbreakBronchopneumoniaDairy cattleVeterinary medicineBacterial pneumoniaBiologyInternal medicineAnimal sciencePediatricsPathology
DOInot available

Abstract

fetched live from OpenAlex

Objective: pneumonia in dairy cows in Ontario. The study objectives were to describe the seasonality, main pathogens involved, and suggested predisposing factors for cases of fatal bacterial bronchopneumonia in mature dairy cows submitted for postmortem examination to a diagnostic laboratory, and to evaluate if the frequency of such submissions has increased over time. Animals: Mature dairy cows. Procedure: Retrospective study of cases submitted for postmortem examination to a diagnostic laboratory from 2007-2020 that were diagnosed as bacterial bronchopneumonia. Results: resistance to antimicrobials used to treat pneumonia. Frequently suggested predisposing factors included recent introductions or movement of animals, recent or imminent calving, inclement weather, concurrent diseases, and poor ventilation in barns. Conclusion and clinical relevance: This study describes seasonal and annual trends, major pathogens, antimicrobial resistance profiles, and suggested predisposing factors in Ontario dairy cows submitted to a diagnostic laboratory for postmortem investigation of pneumonia and provides insights for understanding why outbreaks occur.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.090
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.280
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes2
Has abstractyes

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