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Record W3109479648 · doi:10.3138/jvme-2020-0011

Comparison of Hemorrhagic Complications with Double-ligated versus Auto-ligated Feline Ovarian Pedicles by Fourth-Year Veterinary Students

2020· article· en· W3109479648 on OpenAlexvenueno aff
Annie L. Showers, Stephen J. Horvath, David Pontius, Michelle R. Forman, Audra Hanthorn

Bibliographic record

VenueJournal of Veterinary Medical Education · 2020
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCATSLigationVeterinary medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

The objective of this article is to compare the occurrence of hemorrhagic complications in student-performed feline ovarian pedicle ligations using the traditional suture pedicle double-ligation (PDL) to the suture-less auto-ligation (AL) techniques, and to describe the stepwise method of teaching the AL technique to students. A total of 287 cats underwent an ovariohysterectomy (OHE) performed by a fourth-year veterinary student trained by veterinary faculty to perform the AL technique beginning with a low-fidelity model and progressing to live patient surgeries. Students performed the AL and PDL techniques on 146 and 141 cats respectively. Hemorrhagic complications occurred in 4 of 146 cats (2.7%) in the AL group and 8 of 141 (5.7%) in the PDL group and were not found to be significantly different ( p = 0.2496). This article demonstrates that novice surgeons can safely perform the AL technique on feline ovarian pedicles without significantly increasing complications compared to the traditionally taught method when a stepwise training program is implemented. Additionally, this technique has been shown to be safe, effective, and more efficient when performed by experienced veterinary surgeons. 1 Veterinary institutions should consider including the AL technique in their core curricula as a standard method for feline ovarian pedicle ligation. Doing so will facilitate the development of more proficient entry-level practitioners who are better able to serve their patients, clients, employers, humane societies, and their communities by using a more efficient and safe feline ovariohysterectomy technique.

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)0.001
metaresearch head score (Gemma)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.478
GPT teacher head0.574
Teacher spread0.096 · 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 designNot applicable
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

Citations2
Published2020
Admission routes1
Has abstractyes

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