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Record W2994388969

The use of Sonography for the prediction of pregnancy status in a few canine cases

2011· article· en· W2994388969 on OpenAlexaboutno aff
A. A. Ajadi, JA Oyewusi, IA Adeleye, O. O. Adebayo

Bibliographic record

VenueTropical veterinarian · 2011
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGestational agePregnancyUltrasoundObstetricsOgun stateRadiology
DOInot available

Abstract

fetched live from OpenAlex

Records of twelve dogs presented either for pregnancy confirmation or other obstetrical complaints at the Veterinary Teaching Hospital, University of Agriculture, Abeokuta, Ogun State between July, 2008 and June, 2010 were reviewed. Abdominal ultrasounds were performed in all the dogs in right lateral recumbency using a portable ultrasound machine (Kaixin KX 2000R, Xuzhou, China). In this study, the mean age of the dogs was 2.6 ± 0.85 years (age range = 1.5-4 years).The dogs comprised of Alsatian (6), Alsatian crosses (2), Rottweiler (2) Labrador cross (2), Boerboel (3) and local dogs (2). Gestational sac was observed in 2 (16.7%) dogs, foetal sac in 6 (50%) dogs, foetal skeletal structure in 2 (16.7%) dogs and foetal heart beat in 4 (33.3%) dogs. In four dogs, no sonographic changes were observed and were thus diagnosed as non-pregnant. The mean gestational length (based on the predicted gestational age and the time difference between presentation and parturition) of the dogs was 8.5 ± 0.44 weeks (range = 8-9 weeks). All the dogs that were predicted pregnant eventually delivered with a prediction accuracy of hundred percent. It was therefore concluded that detection of foetal and extra-foetal structures by ultrasonographic examination provides a safe and reliable method of confirming canine pregnancy and predicting gestational age. Trop. Vet . Vol. 29 (4) 1-9 (2011)

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)0.000
metaresearch head score (Gemma)0.000
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 score0.203
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.316
GPT teacher head0.328
Teacher spread0.012 · 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
Published2011
Admission routes1
Has abstractyes

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