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Record W2619036816 · doi:10.12968/coan.2017.22.6.332

Gastrointestinal surgery — some tips and tricks

2017· article· en· W2619036816 on OpenAlexaff
Aylin Atilla

Bibliographic record

VenueCompanion animal · 2017
Typearticle
Languageen
FieldMedicine
TopicIntestinal and Peritoneal Adhesions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineGeneral surgeryEmergency surgeryAbdominal surgeryAbdomenSurgerySurgical procedures

Abstract

fetched live from OpenAlex

Apart from performing an ovariohysterectomy or an ovariectomy, gastrointestinal surgery is the most common type of abdominal surgery that veterinary surgeons perform. It is the most common emergency surgery that most veterinary surgeons perform. The focus of this article is on tips and tricks that can help you perform gastrointestinal surgery; it assumes knowledge of indications as well as how to perform basic procedures. A routine abdominal exploration is the foundation of gastrointestinal surgery; the novice surgeon should identify all lesions present in the abdomen then prioritise them in order of importance and degree of complexity. This practice will allow the surgeon to perform the most important surgical procedures first and minimise unnecessary procedures.

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)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.006
Scholarly communication0.0040.011
Open science0.0020.002
Research integrity0.0060.016
Insufficient payload (model declined to judge)0.0070.007

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 head0.071
GPT teacher head0.314
Teacher spread0.244 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2017
Admission routes1
Has abstractyes

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