MétaCan
Menu
Back to cohort
Record W4385555807 · doi:10.1245/s10434-023-14020-2

ASO Visual Abstract: Impact of Surgical Margin Width on Prognosis Following Resection of Hepatocellular Carcinoma Varies Based on Preoperative Alpha-Feto Protein and Tumor Burden Score

2023· article· en· W4385555807 on OpenAlexaff
Yutaka Endo, Muhammad Musaab Munir, Selamawit Woldesenbet, Erryk Katayama, Francesca Ratti, Hugo P. Marques, François Cauchy, Vincent Lam, George A. Poultsides, Minoru Kitago, Irinel Popescu, Sorin Alexandrescu, Guillaume Martel, Aklile Workneh, Alfredo Guglielmi, Ana Gleisner, Tom Hugh, Luca Aldrighetti, Feng Shen, Itaru Endo, Timothy M. Pawlik

Bibliographic record

VenueAnnals of Surgical Oncology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSurgical oncologyMedicineHepatocellular carcinomaMargin (machine learning)Surgical marginResectionAlpha (finance)Surgical resectionOncologyCarcinomaInternal medicineRadiologySurgery

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.048
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0480.005

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 head0.039
GPT teacher head0.344
Teacher spread0.306 · 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 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

Citations1
Published2023
Admission routes1
Has abstractno

Explore more

Same venueAnnals of Surgical OncologySame topicCancer, Lipids, and MetabolismFrench-language works237,207