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Record W4385064082 · doi:10.1016/s1474-4422(23)00239-9

Peru initiates the IMPACT project

2023· letter· en· W4385064082 on OpenAlexfundno aff
Miriam Lúcar-Flores, José Carlos Vera Tudela, Cecilia Anza‐Ramirez, J. Jaime Miranda, Christopher Butler

Bibliographic record

VenueThe Lancet Neurology · 2023
Typeletter
Languageen
FieldMedicine
TopicLegal, Health, Environmental and COVID-19 Challenges
Canadian institutionsnot available
FundersNational Cancer InstituteFogarty International CenterNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteFondo Nacional de Desarrollo Científico, Tecnológico y de Innovación TecnológicaBiotechnology and Biological Sciences Research CouncilMedical Research CouncilUniversity of North Carolina at Chapel HillAlliance for Health Policy and Systems ResearchWorld Diabetes FoundationPontificia Universidad Católica del PerúInter-American Institute for Global Change ResearchConsejo Nacional de Ciencia, Tecnología e Innovación TecnológicaNational Science FoundationGrand Challenges CanadaNational Institute for Health and Care ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungInternational Development Research CentreBloomberg PhilanthropiesWellcome TrustEngineering and Physical Sciences Research CouncilUK Research and InnovationAlzheimer's Association
KeywordsDementiaContext (archaeology)MedicineHealth carePandemicGlobal healthGerontologyDiseaseEconomic growthPublic healthNursingCoronavirus disease 2019 (COVID-19)GeographyPathology

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)0.005
metaresearch head score (Gemma)0.018
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.084
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0060.004
Open science0.0010.010
Research integrity0.0160.013
Insufficient payload (model declined to judge)0.0840.029

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 head0.117
GPT teacher head0.370
Teacher spread0.253 · 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
GenreEditorial

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

Citations5
Published2023
Admission routes1
Has abstractno

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Same venueThe Lancet NeurologySame topicLegal, Health, Environmental and COVID-19 ChallengesFrench-language works237,207