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Record W2292036658 · doi:10.1016/j.gheart.2015.04.004

Adapting the World Heart Federation Roadmaps at the National Level: Next Steps and Conclusions

2015· article· en· W2292036658 on OpenAlexaff
Pablo Perel, Eduardo Bianco, Neil R Poulter, Dorairaj Prabhakaran, Prem Pais, Johanna Ralston, David Wood, Salim Yusuf

Bibliographic record

VenueGlobal Heart · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsChecklistMedicineObservational studyGlobal healthDownloadStrengthening the reporting of observational studies in epidemiologyEpidemiologyMedical educationPublic healthFamily medicinePathologyPsychologyComputer science

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)0.174
metaresearch head score (Gemma)0.301
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 score0.174
Threshold uncertainty score0.922

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1740.301
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0090.012
Science and technology studies0.0040.004
Scholarly communication0.0170.023
Open science0.0130.015
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0420.021

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 head0.169
GPT teacher head0.362
Teacher spread0.193 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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