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Record W4416109996 · doi:10.1093/bjs/znaf243

Secondary analysis of data from the HeLiX trial regarding the association between estimated blood loss and post-hepatectomy outcomes: towards the definition of a minimal clinically significant difference

2025· article· en· W4416109996 on OpenAlexafffund
Julie Hallet, Tiago C. Ribeiro, Zhi Ven Fong, Giampaolo Perri, Poya Ghorbani, Ernesto Sparrelid, Giovanni Marchegiani, Paul J. Karanicolas, Yulia Lin, Stuart A. McCluskey, Jordan Tarshis, Kevin E. Thorpe, Alice C. Wei, Elijah Dixon, Geoff Porter, Prosanto Chaudhury, Sulaiman Nanji, Leyo Ruo, Melanie E. Tsang, Anton Skaro, Gareth Eeson, Sean P. Cleary, Carol-Anne Moulton, Natalie G. Coburn, Pablo E Serrano, Shiva Jayaraman, Calvin Law, Ved Tandan, Gonzalo Sapisochín, David M. Nagorney, Douglas Quan, Rory L. Smoot, Steven Gallinger, Peter Metrakos, Trevor Reichman, Diederick Jalink, Sean Bennett, Francis Sutherland, Edward Solano, Michele Molinari, Ephraim Tang, Susanne G Warner, Oliver F. Bathe, Jeffrey Barkun, Michael L. Kendrick, Mark J. Truty, Rachel Roke, Grace Xu, Myriam Lafrenière‐Roula, Gordon Guyatt

Bibliographic record

VenueBritish journal of surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchPhysicians' Services Incorporated FoundationCanadian Blood Services
KeywordsMinimal clinically important differenceAssociation (psychology)Blood lossHelix (gastropod)Significant differenceClinical trial

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 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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.226
GPT teacher head0.323
Teacher spread0.097 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2025
Admission routes2
Has abstractno

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