MétaCan
Menu
Back to cohort
Record W4362473358 · doi:10.1016/j.ijid.2023.03.002

Concerns regarding risk factors for SARS-CoV-2 transmission to pets

2023· letter· en· W4362473358 on OpenAlexaff
J. Scott Weese

Bibliographic record

VenueInternational Journal of Infectious Diseases · 2023
Typeletter
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsOdds ratioTransmission (telecommunications)Bivariate analysisConfidence intervalScopusSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Meta-analysisMedicineDemographyCoronavirus disease 2019 (COVID-19)Environmental healthVeterinary medicineStatisticsMEDLINEBiologyDiseaseInternal medicineComputer science

Abstract

fetched live from OpenAlex

I read with interest the paper by Alberto-Orlando et al [[1]Alberto-Orlando S Calderon JL Leon-Sosa A et al.SARS-CoV-2 transmission from infected owner to household dogs and cats is associated with food sharing.Int J Infect Dis. 2022; 122: 295-299https://doi.org/10.1016/j.ijid.2022.05.049Abstract Full Text Full Text PDF PubMed Scopus (15) Google Scholar] entitled “SARS-CoV-2 transmission from infected owner to household dogs and cats is associated with food sharing”. Investigation of human-animal transmission and factors associated with that is an important area of study. The authors report interesting prevalence data, but also an association between food sharing and SARS-CoV-2 infection in pets. That risk factor would be biologically plausible; however, the data and analysis are unclear and do not appear to support that conclusion. Description of the analysis is very superficial and provides little information about the methods. For multivariable analysis, the authors report a significant association between food sharing and infection (P = 0.0025) with an odds ratio of 6.17. Yet, the reported 95% confidence interval (0.22-167.4) is very broad and is inconsistent with a statistically significant result. The bivariate analysis for this variable yielded a P-value of 0.61, and it is surprising that such a change could result from multivariable analysis, especially with such a small sample size and a limited number of variables. This raises the question of whether there was an analysis error as it is very difficult to see how a significant result such as this could be obtained. The author has no competing interests to declare. Concerns regarding risk factors for SARS-CoV-2 transmission to pets: author's replyInternational Journal of Infectious DiseasesVol. 130PreviewWe read with interest the letter by S. Weese entitled "Concerns regarding risk factors for SARS-CoV-2 transmission to pets: Alberto-Orlando et al. 2022" [1]. In this letter, concerns were raised regarding our recent publication “SARS-CoV-2 transmission from infected owner to household dogs and cats is associated with food sharing” [2]. We appreciate the critical reading of our manuscript and we are addressing the concerns in this letter. Full-Text PDF Open Access

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)0.011
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.098
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0070.003

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 head0.056
GPT teacher head0.391
Teacher spread0.335 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Infectious DiseasesSame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207