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2022 AAHA Canine Vaccination Guidelines

2022· article· en· W4294099113 on OpenAlexaff
John Ellis, Elizabeth Marziani, Chumkee Aziz, Catherine Brown, Leah A. Cohn, Christopher Lea, George E. Moore, Neha Taneja

Bibliographic record

VenueJournal of the American Animal Hospital Association · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineVaccinationVaccination scheduleAdverse effectImmunologyImmune systemImmunizationInternal medicine

Abstract

fetched live from OpenAlex

These guidelines are an update and extension of previous AAHA peer-reviewed canine vaccination guidelines published in 2017. Vaccination is a cornerstone of canine preventive healthcare and one of the most cost-effective ways of maintaining a dog's health, longevity, and quality of life. Canine vaccination also serves a public health function by forming a barrier against several zoonotic diseases affecting dogs and humans. Canine vaccines are broadly categorized as containing core and noncore immunizing antigens, with administration recommendations based on assessment of individual patient risk factors. The guidelines include a comprehensive table listing canine core and noncore vaccines and a recommended vaccination and revaccination schedule for each vaccine. The guidelines explain the relevance of different vaccine formulations, including those containing modified-live virus, inactivated, and recombinant immunizing agents. Factors that potentially affect vaccine efficacy are addressed, including the patient's prevaccination immune status and vaccine duration of immunity. Because animal shelters are one of the most challenging environments for prevention and control of infectious diseases, the guidelines also provide recommendations for vaccination of dogs presented at or housed in animal shelters, including the appropriate response to an infectious disease outbreak in the shelter setting. The guidelines explain how practitioners can interpret a patient's serological status, including maternally derived antibody titers, as indicators of immune status and suitability for vaccination. Other topics covered include factors associated with postvaccination adverse events, vaccine storage and handling to preserve product efficacy, interpreting product labeling to ensure proper vaccine use, and using client education and healthcare team training to raise awareness of the importance of vaccinations.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 integrity0.0000.001
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 head0.009
GPT teacher head0.261
Teacher spread0.252 · 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

Citations38
Published2022
Admission routes1
Has abstractyes

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