MétaCan
Menu
Back to cohort
Record W1766405961 · doi:10.1016/j.gheart.2014.10.006

World Heart Federation Emerging Leaders Program: An Innovative Capacity Building Program to Facilitate the 25 × 25 Goal

2015· article· en· W1766405961 on OpenAlexaff
Mark D. Huffman, Pablo Perel, George Beller, Lucy Keightley, J. Jaime Miranda, Johanna Ralston, K. Srinath Reddy, David A. Wood, Darwin R. Labarthe, Salim Yusuf

Bibliographic record

VenueGlobal Heart · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersMedical Research Council
KeywordsChecklistMedicineGlobal healthObservational studyStrengthening the reporting of observational studies in epidemiologyMedical educationPublic healthPublic relationsFamily medicinePolitical sciencePathologyPsychology

Abstract

fetched live from OpenAlex

Global Heart is the official and primary publication of the World Heart Federation, offering a platform for the dissemination of knowledge on research, developments, trends, solutions and public health programmes in the area of cardiovascular disease. Global Heart welcomes research results, points of view and educational material on the prevention, treatment and control of cardiovascular disease with a special focus on low and middle-income countries which are facing the brunt of epidemiological transition.Global Heart strongly encourages authors to adhere to CONSORT, STROBE, STARD, and PRISMA guidelines for reporting of clinical trials, observational studies, diagnostic test accuracy papers, and systematic reviews or meta-analyses. Authors are required for submission to download and complete the appropriate Equator Network checklist: http://www.equator-network.org/.

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)0.109
metaresearch head score (Gemma)0.098
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: Empirical · Consensus signal: none
Teacher disagreement score0.109
Threshold uncertainty score0.577

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1090.098
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0060.003
Scholarly communication0.0100.008
Open science0.0060.032
Research integrity0.0050.012
Insufficient payload (model declined to judge)0.1070.044

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 head0.170
GPT teacher head0.389
Teacher spread0.218 · 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
GenreEmpirical

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

Citations8
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueGlobal HeartSame topicGlobal Public Health Policies and EpidemiologyFrench-language works237,207