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Record W2945156377 · doi:10.3389/fpsyt.2019.00345

Individualized Prediction of Transition to Psychosis in 1,676 Individuals at Clinical High Risk: Development and Validation of a Multivariable Prediction Model Based on Individual Patient Data Meta-Analysis

2019· review· en· W2945156377 on OpenAlexaff
Aaltsje Malda, Nynke Boonstra, Hans Barf, Steven de Jong, André Alemán, Jean Addington, Marita Pruessner, Dorien H. Nieman, Lieuwe de Haan, Anthony P. Morrison, Anita Riecher‐Rössler, Erich Studerus, Stephan Ruhrmann, Frauke Schultze‐Lutter, Suk Kyoon An, Shinsuke Koike, Kiyoto Kasai, Barnaby Nelson, Patrick D. McGorry, Stephen J. Wood, Ashleigh Lin, Magdalena Kotlicka‐Antczak, Marco Armando, Stefano Vicari, Masahiro Katsura, Kazunori Matsumoto, Sarah Durston, Tim Ziermans, Lex Wunderink, Helga Ising, Mark van der Gaag, Paolo Fusar‐Poli, Gerdina Hendrika Maria Pijnenborg

Bibliographic record

VenueFrontiers in Psychiatry · 2019
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteHotchkiss Brain InstituteUniversity of Calgary
FundersUniversität zu KölnJapan Society for the Promotion of ScienceNational Health and Medical Research CouncilNational Research Foundation of KoreaMedical Research CouncilGGZ DrentheNational Research FoundationUniversity of TokyoNational Alliance for Research on Schizophrenia and DepressionZonMwRijksuniversiteit GroningenSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungGGZ FrieslandKing's College LondonBundesministerium für Bildung und ForschungNational Institute for Health and Care ResearchJapan Agency for Medical Research and DevelopmentSouth London and Maudsley NHS Foundation TrustNational Science FoundationStanley Medical Research InstituteMinistry of Education, Culture, Sports, Science and TechnologyNational Institute of Mental HealthFondation pour la Recherche MédicaleBrain and Behavior Research Foundation
KeywordsMultivariable calculusMeta-analysisPsychosisPatient dataPsychologyMedicineClinical psychologyComputer sciencePsychiatryInternal medicineEngineering

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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.613
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.146
GPT teacher head0.388
Teacher spread0.242 · 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.

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

Citations39
Published2019
Admission routes1
Has abstractyes

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