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Record W4414815242 · doi:10.1007/s00520-025-09993-7

Health utility book: A systematic review and meta-analysis of health utilities in gastric cancer

2025· review· en· W4414815242 on OpenAlexaff
Shant Torkom Yeretzian, Brittany Humphries, Meixuan Li, Chiranjeev Sanyal, Gaurav Talwar, Michael Xu, Peijing Yan, Frances Simbulan, Zhiyuan Chen, Wang Pei, Ting Zhou, Veena Manja, Xuejing Jin, Yuan Sun, Preston Tse, Michael J. Zoratti, Shitong Xie, Feng Xie

Bibliographic record

VenueSupportive Care in Cancer · 2025
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of TorontoDalhousie UniversitySickKids FoundationMcMaster University Medical CentreHospital for Sick ChildrenMcMaster University
Fundersnot available
KeywordsCancerSelection (genetic algorithm)Nursing researchPain medicineHealth economicsSystematic reviewMEDLINESet (abstract data type)

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)0.008
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0100.011
Bibliometrics0.0070.009
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.151
GPT teacher head0.400
Teacher spread0.249 · 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 designMeta-analysis
Domainnot available
GenreReview

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 routes1
Has abstractno

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