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Record W3155820287

Standing laparoscopic abdominal lavage using a suction-irrigation device in 2 horses with primary suppurative peritonitis.

2021· article· en· W3155820287 on OpenAlexaff
Nicola Cribb, Luis G. Arroyo, Ludovic Bouré

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldMedicine
TopicDiverticular Disease and Complications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineAbdomenSuctionSurgeryLaparoscopyPeritonitisTherapeutic irrigation
DOInot available

Abstract

fetched live from OpenAlex

The use of a laparoscopic suction-irrigation device in 2 standing horses for lavage of the abdomen for the treatment of primary suppurative peritonitis is reported. Two horses were presented with a 1- to 2-week history of weight loss. Abdominocentesis revealed highly elevated total nucleated cell count. Peritoneal lavage systems were placed in both horses, but complications prevented adequate lavage. Both horses underwent standing laparoscopy; the dorsal abdomen was explored and the abdomen was profusely lavaged, using a suction-irrigation device. The procedure was efficient and allowed adequate visualization of the dorsal abdomen and lavage. A successful outcome was achieved in both cases. Key clinical message: Lavage of the abdomen of horses with peritonitis can be achieved under standing sedation, using a laparoscopic technique. In appropriately selected cases, this allows for adequate visualization of the dorsal abdomen and efficacious abdominal lavage.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0050.002
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 head0.035
GPT teacher head0.263
Teacher spread0.228 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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