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Record W4285390610 · doi:10.1038/s41572-022-00376-4

Multimorbidity

2022· review· en· W4285390610 on OpenAlexafffund
Søren Thorgaard Skou, Frances S Mair, Martin Fortin, Bruce Guthrie, Bruno Pereira Nunes, J. Jaime Miranda, Cynthia M. Boyd, Sanghamitra Pati, Sally Mtenga, Susan M. Smith

Bibliographic record

VenueNature Reviews Disease Primers · 2022
Typereview
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversité de Sherbrooke
FundersNational Heart, Lung, and Blood InstituteNational Institutes of HealthFondo Nacional de Desarrollo Científico, Tecnológico y de Innovación TecnológicaWellcome TrustBiotechnology and Biological Sciences Research CouncilMedical Research CouncilAlliance for Health Policy and Systems ResearchWorld Diabetes FoundationConsejo Nacional de Ciencia, Tecnología e Innovación TecnológicaFogarty International CenterCanadian Institutes of Health ResearchUK Research and InnovationNational Science FoundationGrand Challenges CanadaUniversity of North Carolina at Chapel HillEuropean CommissionRegion SjællandNational Institute on AgingHarvard T.H. Chan School of Public HealthNational Institute for Health and Care ResearchDirectorate for Biological SciencesSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Institute of Mental HealthInternational Development Research CentreBloomberg PhilanthropiesNational Cancer InstituteInter-American Institute for Global Change ResearchEngineering and Physical Sciences Research Council
KeywordsMultimorbidityPsychological interventionPsychosocialMedicineHealth careSocioeconomic statusQuality of life (healthcare)GerontologyPopulationNursingEnvironmental healthPsychiatryEconomic growth

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.007

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 head0.458
Teacher spread0.291 · 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 designNot applicable
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

Citations1,306
Published2022
Admission routes2
Has abstractno

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