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Record W4302028664 · doi:10.1002/vrc2.505

Medical management of gastric distension secondary to massive dry beet pulp ingestion in a dog

2022· article· en· W4302028664 on OpenAlexaff
Katie M. Janse van Vuuren, Meagan Walker, Katie Hoddinott

Bibliographic record

VenueVeterinary Record Case Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicIntestinal and Peritoneal Adhesions
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsMedicineIngestionBeet pulpDistensionComplicationAbdominal distensionSurgeryPulp (tooth)AnesthesiaDentistryInternal medicineAnimal science

Abstract

fetched live from OpenAlex

Abstract A 4‐year‐old, spayed, female Jack Russell terrier dog presented after ingestion of 0.135 kg of dried beet pulp pellets. Survey radiographs revealed severe gastric distension. The dog was hospitalised and received intravenous fluids, analgesics and an antiemetic and recovered without complication. To the authors’ knowledge, this is the first reported case of medical management of gastric distension secondary to beet pulp ingestion in a dog. This case illustrates that surgical intervention of severe gastric distension secondary to dry beet pulp ingestion may not be required. Close monitoring for evidence of gastric obstruction or perforation is warranted.

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 categoriesInsufficient payload (model declined to judge)
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 score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.303
Teacher spread0.274 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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