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Record W4415623463 · doi:10.1007/s00464-025-12339-3

International expert consensus-driven surgical process model for robot-assisted hysterectomy: Delphi study results

2025· article· en· W4415623463 on OpenAlexaff
Krystel Nyangoh Timoh, Soline Galuret, Thomas Hébert, Henri Azaïs, Marc Barahona, Sven Becker, Pierre‐Adrien Bolze, Thomas Boisramé, Bruno Borghese, Marie Carbonnel, Vito Cela, Céline Chauleur, Tony Chalhoub, Tak Hong Cheung, François Closon, Patrice Crochet, Yohann Dabi, Laurent de Landsheere, Émilie Faller, Francesco Fanfani, Tristan Gauthier, Walter H. Gotlieb, Manuel Maria Ianieri, Thomas Ind, Tae Joong Kim, Martin Koskas, Joseph Ng, Benjamin Merlot, Jiheum Paek, Charles‐André Philip, Diego Raimondo, Horace Roman, Maria Rosendal, Renato Seracchioli, Tommaso Simoncini, Phuong Lien Tran, Claire Tourette, Arnaud Huaulmé, Pierre Jannin

Bibliographic record

VenueSurgical Endoscopy · 2025
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsProcess (computing)Delphi methodDelphiExpert systemRepresentation (politics)

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.068
metaresearch head score (Gemma)0.105
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.105
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.073
GPT teacher head0.412
Teacher spread0.339 · 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 designQualitative
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
Published2025
Admission routes1
Has abstractno

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