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Feeding Patterns and Medical Diagnosis Frequency in a National Survey of Older Dogs

2020· article· en· W3018476045 on OpenAlexaboutno aff
Regina Hollar, Heidi Schiefelbein, Becky A. Stone, Jennifer M. MacLeay, Susan M. Wernimont

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsLabrador RetrieverMedicineBreedGerman Shepherd DogBeagleVeterinary medicineMedical historyPhysical examinationAnimal welfareFamily medicinePediatricsDemographySurgeryAnimal scienceInternal medicine

Abstract

fetched live from OpenAlex

Veterinarians and owners are often concerned with the nutrition of dogs, but most knowledge of real‐world pet feeding practices is anecdotal. A survey was conducted among owners of dogs aged ≥ 5 yrs at general practice veterinary clinics across the United States (2016–2017). Owners participated in a clinic visit with their dog, and demographics including age and breed were collected. Veterinarians documented the dog’s medical history and completed a physical exam including Body Fat Index (BFI) rated using a visual scale. Owners completed a nutrition survey by estimating the proportions of dry, wet, human food, treats, and other foods (including homemade petfoods) offered to their dog the prior day. Complete nutrition survey information was obtained from 992 dogs (5.0 to 20.8 yrs, median 9.4 yrs); of these 982 (99%) had complete medical histories. Dogs represented 15 states: PA (17%), CO (17%), NY (12.7%), IL (8.5%), VA (7.9%), FL (7.8%), MO (6.8%), NJ (5.0%), MI (4.6%), MD (4.5%), TX (3.3%), LA (2.5%), GA (1.5%), CT (0.6%), and CA (0.2%). In addition to mixed breeds, 115 dog breeds were reported by owners, most commonly: Labrador Retriever (13.1%), Golden Retriever (5.3%), Shih Tzu (3.9%), Other (3.7%), German Shepherd (3.7%), Dachshund (3.3%), Yorkshire Terrier (3.1%), Beagle (2.7%), Bichon Frise (2.6%) and Pug (2.4%). Giant breeds together represented 3.8% of all dogs. The 10 most common diagnoses observed in the medical history were mild/moderate dental/gum disease: 83.7%, benign tumors (including lipomas): 46.2%, overweight/obese: 34.8%, otitis (in which atopic dermatitis was ruled out): 20.3%, arthritis (elbow, stifle, back, and/or neck): 19.6%, atopic dermatitis: 15.3%, hypothyroidism: 13.1%, sebaceous cysts: 11.3%, arthritis of the hip: 10.7%, urinary tract infection: 10.0%. BFI as assessed by vets at the time of the survey ranged from 10 to 70, median 30; 69.2% dogs had a BFI of 20 or 30 while 23.5% had a BFI of 40 or more. 92.5% of dogs were offered any dry food while 72.6% of dogs were offered ≥ 90% of their intake as dry food and 19.5% of dogs were offered exclusively dry food. 23.6% of dogs were offered any wet food; 2.6% were offered ≥ 90% of their intake as wet food and only 0.7% of dogs were offered exclusively wet food. 58.8% of dogs were offered any treats, 32.0% of dogs were offered any human food, and 5.0% were offered other food, including homemade petfoods. Most owners of older dogs fed predominantly dry food and only a minority fed wet food exclusively. Treats and human food were commonly offered. The frequency of breeds and diagnoses observed in this population were generally similar to other national surveys; this analysis provides important information about feeding patterns among older dogs. Support or Funding Information This study was funded by Hill’s Pet Nutrition, Inc.

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 score0.013
Threshold uncertainty score0.027

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.049
GPT teacher head0.348
Teacher spread0.299 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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