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Record W2947740670 · doi:10.1177/1098612x19850973

Influence of time and phenotype on salivary Fel d1 in domestic shorthair cats

2019· article· en· W2947740670 on OpenAlexaboutno aff
Berenice Camille Bastien, Cari Gardner, Ebenezer Satyaraj

Bibliographic record

VenueJournal of Feline Medicine and Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsnot available
Fundersnot available
KeywordsCATSSalivaMedicineVeterinary medicinePhysiologyInternal medicineAnimal scienceBiology

Abstract

fetched live from OpenAlex

Objectives Fel d1 is a major allergen that may affect humans sensitive to cat allergens, and it can be detected in the saliva and on the hair of cats. We studied the variability of salivary Fel d1 in typical house cats (ie, neutered domestic shorthair cats) and the factors that could be associated with that variability. Methods Saliva samples were collected from 64 cats, twice daily, every other day, for a year, at two locations (Missouri, USA, and Ontario, Canada). Salivary Fel d1 levels were measured using an immunoassay. Correlations and linear mixed-effects model analyses were run to assess which factors significantly affected the Fel d1 levels. Results Salivary Fel d1 levels varied significantly both within and among cats. Cat averages over the year ranged from 0.4–35 µg/ml, and a higher average correlated with a higher SD ( P <0.001). The first collection of the day tended to be higher than the afternoon collection ( P <0.001). Sex, coat color or body size did not relate to cats’ average Fel d1 production, but older cats tended to have lower salivary Fel d1 levels ( P <0.001). Fel d1 levels from four samples were reliable in identifying cats producing stable low levels of Fel d1. Conclusions and relevance We observed a wide and continuous range of salivary Fel d1 production in domestic shorthair cats. In particular, a subset of cats had stable low levels throughout the course of the year, and they can be identified by analyzing a few saliva samples rather than their physical appearance.

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)0.001
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 score0.066
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.271
Teacher spread0.255 · 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

Citations16
Published2019
Admission routes1
Has abstractyes

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