MétaCan
Menu
Back to cohort
Record W2807834487 · doi:10.1016/s2214-109x(18)30281-x

Adiposity and mortality in South Asians: challenges to the existing paradigm

2018· letter· en· W2807834487 on OpenAlexaff
Darryl P. Leong, Salim Yusuf

Bibliographic record

VenueThe Lancet Global Health · 2018
Typeletter
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsHamilton General HospitalPopulation Health Research Institute
Fundersnot available
KeywordsBody mass indexDemographyScopusMedicineDiseaseEthnic groupMortality rateMeta-analysisGerontologyMEDLINEInternal medicinePolitical science

Abstract

fetched live from OpenAlex

In 2010, the Global Burden of Disease study estimated that 3·4 million deaths were attributable to a body-mass index (BMI) above 21–23 kg/m2.1 These estimates were based on data that were predominantly from Europeans and east Asians, with few data from other ethnic groups.2 There was a positive relation between BMI (when >25 kg/m2) and cardiovascular disease, respiratory disease, cancer, and mortality.2 These estimates are robust in Europeans (56 477 deaths) and east Asians (100 784 deaths) but there was no apparent association in South Asians.

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)0.132
metaresearch head score (Gemma)0.112
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: Commentary · Consensus signal: none
Teacher disagreement score0.132
Threshold uncertainty score0.696

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1320.112
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0070.009
Science and technology studies0.0040.014
Scholarly communication0.0090.019
Open science0.0070.013
Research integrity0.0060.020
Insufficient payload (model declined to judge)0.0080.002

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 head0.136
GPT teacher head0.391
Teacher spread0.256 · 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
GenreCommentary

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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe Lancet Global HealthSame topicBirth, Development, and HealthFrench-language works237,207