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Record W2907285051 · doi:10.3324/haematol.2018.209114

The Khorana score for prediction of venous thromboembolism in cancer patients: a systematic review and meta-analysis

2019· review· en· W2907285051 on OpenAlexfundno aff
Frits I. Mulder, Matteo Candeloro, Pieter W. Kamphuisen, Marcello Di Nisio, Patrick M. Bossuyt, Noori A.M. Guman, Kirsten Smit, Harry R. Büller, Nick van Es

Bibliographic record

VenueHaematologica · 2019
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsnot available
FundersUniversitätsklinikum KölnTianjin Medical UniversityUniversity College London Hospitals NHS Foundation TrustMedizinische Universität WienNational and Kapodistrian University of AthensUniversità degli Studi di Roma Tor VergataUniversitair Medisch Centrum GroningenCleveland ClinicUniversity College LondonUniversity of TorontoUniversity of Oklahoma Health Sciences CenterUniversität WienCardiff UniversityUniversity of WashingtonUniversity of OklahomaSamsungPusan National UniversityUniversity of OttawaOhio State University
KeywordsMedicineAmbulatoryInternal medicineIncidence (geometry)Framingham Risk ScoreMeta-analysisVenous thromboembolismThrombosisDisease

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.017
metaresearch head score (Gemma)0.038
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: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.038
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0180.036
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.167
GPT teacher head0.382
Teacher spread0.215 · 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

Citations322
Published2019
Admission routes1
Has abstractyes

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