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Record W4389805269 · doi:10.3791/65742

Robotic D3 Partial Duodenal Resection with Primary Side-to-Side Anastomosis

2023· article· en· W4389805269 on OpenAlexaff
Roberto Maria Montorsi, Sofia Xenaki, Sebastiaan Festen, Paul Fockens, Barbara A.J. Bastiaansen, Freek Daams, Olivier R. Busch, Marc G. Besselink

Bibliographic record

VenueJournal of Visualized Experiments · 2023
Typearticle
Languageen
FieldMedicine
TopicIntestinal Malrotation and Obstruction Disorders
Canadian institutionsPancreas Centre (Canada)
FundersAmsterdam University Medical Centers
KeywordsMedicinePancreaticoduodenectomySuperior mesenteric arteryAnastomosisSurgeryStenosisDissection (medical)DuodenumResectionRadiology

Abstract

fetched live from OpenAlex

Duodenal stenosis is a condition that can be related to several diseases, being either intrinsic, such as neoplasm and inflammatory stenosis, or extrinsic, such as pancreatic pseudocyst, superior mesenteric artery syndrome, and foreign bodies. Current treatments range from endoscopic approaches, such as endoscopic resection and stent placement, to surgical approaches, including duodenal resection, pancreaticoduodenectomy, and gastrointestinal bypass. Minimally invasive robot-assisted surgery is gaining importance due to its potential to decrease surgical stress, intraoperative blood loss, and postoperative pain, while its instruments and 3D-vision facilitate fine dissection and intra-abdominal suturing, all leading to a reduced time to functional recovery and shorter hospital stay. We present a case of a 75-year-old female who underwent robotic D3 partial duodenal resection with primary side-to-side duodeno-jejunal anastomosis for a 5 cm adenoma with focal high-grade dysplasia.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.404
Teacher spread0.368 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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