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Record W4407585361 · doi:10.9734/jabb/2025/v28i21995

Epidemiological Assessment of Systemic Hypertension in Dogs

2025· article· en· W4407585361 on OpenAlexaboutno aff
R.K. Tyagi, Devendra Gupta, Shashi Pradhan, S. M. Tripathi, Apra Shahi, Salil Kumar Pathak, Aditya Pratap, Riya Mathur, Harshit Kaur Sachdev

Bibliographic record

VenueJournal of Advances in Biology & Biotechnology · 2025
Typearticle
Languageen
FieldMedicine
TopicChemokine receptors and signaling
Canadian institutionsnot available
Fundersnot available
KeywordsBreedMedicineBlood pressureVeterinary medicineEpidemiologyAnimal husbandryInternal medicineAnimal scienceBiology

Abstract

fetched live from OpenAlex

Systemic hypertension in dogs is a clinically significant condition often associated with underlying diseases. The objective of this study was to assess the prevalence and distribution of hypertension in dogs. A total of 214 dogs (149 males and 65 females, irrespective of age and breed) were surveyed randomly at the Veterinary Clinical Complex, College of Veterinary Science & Animal Husbandry, Nanaji Deshmukh Veterinary Science University, Jabalpur, Madhya Pradesh, India, between May and October 2024. This study comprised of 81 clinically healthy dogs and 133 dogs diagnosed with various systemic co-morbidities. Blood pressure measurements were performed using a Doppler Vet BP machine following ACVIM guidelines (Acierno et al.,2018). Clinical Hypertension (SAP ≥160 mmHg) using Doppler NIBP was recorded in 39 dogs, resulting in an overall prevalence of 18.22%. Secondary hypertension was more common (27.81%) compared to primary hypertension (2.46%), reinforcing the strong association between hypertension and concurrent diseases. Age-wise analysis revealed a higher prevalence in dogs over 8 years (30%), suggesting an age-related predisposition. Male dogs (76.92%) were more frequently affected than females (23.07%). Breed predisposition was observed, with Labrador Retrievers (33.3%) and German Shepherds (23.07%) showing the highest occurrence.

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 score0.107
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.015
GPT teacher head0.364
Teacher spread0.349 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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