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Optimal sampling in derivation studies was associated with improved discrimination in external validation for heart failure prognostic models

2020· review· en· W3004255156 on OpenAlexaff
Naotsugu Iwakami, Toshiyuki Nagai, Toshi A. Furukawa, Aran Tajika, Akira Ōnishi, Kunihiro Nishimura, Soshiro Ogata, Michikazu Nakai, Misa Takegami, Hiroki Nakano, Yohei Kawasaki, Ana Carolina Alba, Gordon Guyatt, Yasuyuki Shiraishi, Shun Kohsaka, Takashi Kohno, Ayumi Goda, Atsushi Mizuno, Tsutomu Yoshikawa, Toshihisa Anzai

Bibliographic record

VenueJournal of Clinical Epidemiology · 2020
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsImpactMcMaster UniversityUniversity of Toronto
FundersJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyJapan Cardiovascular Research FoundationJapan Agency for Medical Research and Development
KeywordsMedicineStatisticPopulationCohortStatisticsCritical appraisalCohort studyExternal validitySampling (signal processing)Internal medicineMathematicsPathologyComputer science

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)0.064
metaresearch head score (Gemma)0.138
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.138
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.496
GPT teacher head0.546
Teacher spread0.050 · 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.

Study designMeta-analysis
DomainMethods
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

Citations4
Published2020
Admission routes1
Has abstractno

Explore more

Same venueJournal of Clinical Epidemiology→Same topicHeart Failure Treatment and Management→French-language works237,207→