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Record W4298088051 · doi:10.1111/jvim.16549

Noninvasive hydrostatic reduction of an ileocecocolic intussusception in a puppy

2022· article· en· W4298088051 on OpenAlexaff
Geoffrey Neumann, Evence Daure

Bibliographic record

VenueJournal of Veterinary Internal Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal disorders and treatments
Canadian institutionsCanadian Veterinary Medical AssociationUniversité de MontréalCegep de Saint Hyacinthe
Fundersnot available
KeywordsPuppyMedicineIntussusception (medical disorder)Reduction (mathematics)Surgery

Abstract

fetched live from OpenAlex

A 5-week-old male intact Golden Retriever puppy was presented for a history of vomiting and diarrhea with hematochezia. Ultrasound findings confirmed the presence of an ileocecocolic intussusception. Surgical correction was declined because of financial concerns. Based on a pediatric procedure used in humans, an ultrasound-guided hydrostatic reduction (USGHR) was performed. This procedure consisted in injecting saline rectally under controlled pressure to mechanically reduce the intussusception. Reduction of the intussusception and evaluation of potential complications were concurrently evaluated by ultrasound during the procedure. No recurrence was observed the next day and the puppy was discharged. Follow-up indicated that the dog was still doing well 6 months later. This case report describes a new technique in veterinary medicine allowing successful nonsurgical reduction of an ileocecocolic intussusception in a dog. This procedure is innovative, simple, and substantially decreases the cost and minimizes morbidity potentially associated with surgical management.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.031
GPT teacher head0.331
Teacher spread0.300 · 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 designCase report
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

Citations2
Published2022
Admission routes1
Has abstractyes

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