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Record W4384924378 · doi:10.1016/s2214-109x(23)00284-x

The global challenge of childhood obesity and its consequences: what can be done?

2023· article· en· W4384924378 on OpenAlexaff
Zulfiqar A Bhutta, Shane A. Norris, Morven Roberts, Atul Singhal

Bibliographic record

VenueThe Lancet Global Health · 2023
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsSickKids FoundationCentre for Global Health ResearchHospital for Sick Children
Fundersnot available
KeywordsChildhood obesityObesityEnvironmental healthGlobal healthMedicinePolitical scienceGeographyPublic healthOverweightNursingEndocrinology

Abstract

fetched live from OpenAlex

Although there is concern over the changing epidemiology and increasing trends of obesity in childhood and adolescence, which has risen steadily over the past decade,1 including a robust call for action by WHO,2 progress in reducing the burden at a global level has been negligible. Of greater concern is the recognition that the relative increase in overweight and obesity is greater among poorer sections of the population and in rural areas,3 representing nutrition transition that foretells a future increase in the burden of non-communicable diseases in low-income and middle-income countries (LMICs).

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)0.016
metaresearch head score (Gemma)0.040
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.029
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.040
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0030.003
Science and technology studies0.0040.010
Scholarly communication0.0120.018
Open science0.0040.009
Research integrity0.0160.018
Insufficient payload (model declined to judge)0.0290.007

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 head0.062
GPT teacher head0.359
Teacher spread0.297 · 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

Citations26
Published2023
Admission routes1
Has abstractyes

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