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Record W2463986804 · doi:10.1371/journal.pmed.1002056

Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement

2016· article· en· W2463986804 on OpenAlexaff
Gretchen A Stevens, Leontine Alkema, Robert E. Black, J. Ties Boerma, Gary S. Collins, Majid Ezzati, John Grove, Daniel Hogan, Margaret C. Hogan, Richard Horton, Joy E Lawn, Ana Marušić, Colin Mathers, Christopher J L Murray, Igor Rudan, Joshua A. Salomon, Paul J. Simpson, Theo Vos, Vivian Welch

Bibliographic record

VenuePLoS Medicine · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCentre for Global Health ResearchBruyèreUniversity of Ottawa
FundersWellcome TrustWorld Health OrganizationBill and Melinda Gates Foundation
KeywordsStatement (logic)MedicineMEDLINEData scienceComputer scienceEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

Gretchen Stevens and colleagues present the GATHER statement, which seeks to promote good practice in the reporting of global health estimates.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.240
metaresearch head score (Gemma)0.498
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.760
Threshold uncertainty score0.937

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2400.498
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0120.014
Science and technology studies0.0020.004
Scholarly communication0.0100.009
Open science0.0070.010
Research integrity0.0120.012
Insufficient payload (model declined to judge)0.0340.039

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 head0.843
GPT teacher head0.572
Teacher spread0.271 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
GenreMethods

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

Citations481
Published2016
Admission routes1
Has abstractyes

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