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Record W4415183453 · doi:10.1016/j.cpsurg.2025.101917

Revolutionizing small bowel transplantation in mice and rats: A cutting-edge dry-lab simulation model

2025· article· en· W4415183453 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Problems in Surgery · 2025
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsUniversity of British Columbia
FundersJung-Stiftung für Wissenschaft und ForschungBundesministerium für Bildung und Forschung
KeywordsAnastomosisAnimal modelUsabilityTransplantationVirtual realityHaptic technologyResectionInflammatory Bowel Diseases

Abstract

fetched live from OpenAlex

Background Animal reduction is a key ethical goal in research. Small bowel transplantation in rodents is essential for studying rejection but training consumes many animals without scientific yield. Methods We developed a low-cost simulation model using silicone-coated intravenous lines sized to mouse vasculature (SMA, portal vein, aorta, vena cava). Eight experienced microsurgeons performed anastomoses and rated usability, similarity, and material quality (5-point Likert). Results The model allowed realistic training in suturing and vessel handling. Arterial components mimicked tissue well, while venous models were overly rigid. Usability was rated high, procedural similarity moderate–positive. Suturing and procedure times resembled live surgery, though haptic feedback and knot security were limited. Conclusion This reproducible dry-lab model supports early microsurgical training and reduces animal use. Despite material limitations, it provides a practical, ethical tool for foundational skill development.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.330
Teacher spread0.267 · 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