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Record W3042960493

Retrospective evaluation on the outcome of perineal herniorrhaphy augmented with porcine small intestinal submucosa in dogs and cats.

2020· article· en· W3042960493 on OpenAlexaff
Natalie Swieton, Ameet Singh, Daniel J. Lopez, Michelle L. Oblak, Katie Hoddinott

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineCATSMedical recordSurgerySubmucosaRetrospective cohort studyAbdominal surgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate post-operative outcome in dogs and cats undergoing perineal herniorrhaphy using porcine small intestinal submucosa (PSIS) alone and with internal obturator muscle transposition augmented with PSIS (IOMT + PSIS). Medical records were retrospectively reviewed and information collected on signalment, pre-operative signs, operative details, and hospitalization. Data on post-operative outcome were obtained from medical records and survey. Eleven dogs and 3 cats had 18 perineal hernias repaired with IOMT + PSIS and 3 using PSIS alone. Surgical site infection developed following IOMT + PSIS in 1/21 hernias (5.6%). Short- and long-term postoperative complications occurred in 9/14 animals and 3/14 animals, respectively. Among the 21 perineal hernias, 3 recurred, 2 of which were repaired with IOMT + PSIS and 1 with PSIS alone. Use of PSIS alone or augmenting IOMT was acceptable for perineal herniorrhaphy and should be considered by surgeons if there are concerns about internal obturator muscle integrity.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.066
GPT teacher head0.253
Teacher spread0.188 · 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 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

Citations13
Published2020
Admission routes1
Has abstractyes

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