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Record W4225089217 · doi:10.1111/jvim.16429

Gastrointestinal foreign bodies in pet pigs: 17 cases

2022· article· en· W4225089217 on OpenAlexaff
Yoko Nakamae, Kallie J. Hobbs, Jessie Ziegler, Luis A. Rivero, Shari Kennedy, Jenna Stockler, Diego E. Gómez

Bibliographic record

VenueJournal of Veterinary Internal Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicForeign Body Medical Cases
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineForeign BodiesDermatologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Pigs have an indiscriminate eating behavior placing them at high risk of developing foreign body (FB) obstructions. OBJECTIVES: Describe the clinical and diagnostic features, treatments, and outcome of pet pigs diagnosed with gastrointestinal (GI) FBs. Medical and surgical treatments, pig outcomes, and post-mortem findings were also investigated. ANIMALS: Seventeen pet pigs. METHODS: A multicenter retrospective study was conducted. Gastrointestinal FBs were defined as swallowed objects that became lodged within the gastrointestinal tract distal to the cardia identified during exploratory laparotomy. RESULTS: Common clinical signs were anorexia/hyporexia, tachypnea, vomiting, dehydration, tachycardia, and ileus. Diagnostic imaging identified the presence of a FB in 4 cases. Upon celiotomy, the FBs were in the stomach and small intestine in 17 cases and large colon in 2 cases. Types of FB included fruit pit, diaper, and metallic objects. Of the 17 pigs, 15 (88%) were discharged from the hospital and 2 (12%) were euthanized. CONCLUSION AND CLINICAL IMPORTANCE: Clinical signs of GI FB were similar to those reported in obstipated pigs. Diagnostic imaging has limitations for detection of FB. Surgical removal of FBs in pigs carried a good prognosis.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.342
Teacher spread0.280 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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