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Record W2789522870 · doi:10.1111/vsu.12766

Evaluation of pet owner preferences for operative sterilization techniques in female dogs within the veterinary community

2018· article· en· W2789522870 on OpenAlexaff
Christine Hsueh, Michelle A. Giuffrida, Philipp D. Mayhew, J. Brad Case, Ameet Singh, Eric Monnet, David E. Holt, Megan Cray, Chiara Curcillo, Jeffrey J. Runge

Bibliographic record

VenueVeterinary Surgery · 2018
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineLaparoscopyPort (circuit theory)Veterinary medicineSterilization (economics)General surgeryMinimally invasive proceduresInvasive surgeryPopulationSurgeryEnvironmental healthCurrency

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe pet owner preferences within the veterinary community when choosing operative techniques for canine spay. STUDY DESIGN: Prospective survey. SAMPLE POPULATION: 1234 respondents from 5 veterinary university teaching hospitals in North America. METHODS: An electronic survey was distributed to faculty, students, and staff that currently are or previously were dog owners. Responses were analyzed to determine what spay technique respondents would choose for their own dogs. Surgical options offered included open celiotomy, 2-port (TP) laparoscopy, single-port (SP) laparoscopy, and natural orifice transluminal endoscopic surgery (NOTES). RESULTS: TP laparoscopic ovariectomy (OVE) was the most popular choice, followed by SP laparoscopic OVE; NOTES was the least popular technique when all surgical options were available. If only minimally invasive surgeries were offered, 0.3% of respondents would refuse surgery. Nearly half (48%) of respondents were willing to spend between $100 and $200 more for a minimally invasive OVE than for an open celiotomy. CONCLUSION: Minimally invasive OVE is an acceptable operative approach to those in the veterinary community. Additional study is required to correlate these findings with the general veterinary client population.

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.350
GPT teacher head0.429
Teacher spread0.079 · 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.

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
Published2018
Admission routes1
Has abstractyes

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