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Record W4404603036 · doi:10.1016/j.eclinm.2024.102958

Burden of disease attributable to high body mass index: an analysis of data from the Global Burden of Disease Study 2021

2024· erratum· en· W4404603036 on OpenAlexaff
Xiaodong Zhou, Qin‐Fen Chen, Wah Yang, Mauricio Zuluaga, Giovanni Targher, Christopher D. Byrne, Luca Valenti, Fei Luo, Christos S. Katsouras, Omar Thaher, Anoop Misra, Karim Ataya, Rodolfo J. Oviedo, Alice P.S. Kong, Khalid Alswat, Amedeo Lonardo, Yu Jun Wong, Adam Abu-Abeid, Hazem Al Momani, Arshad Ali, Gabriel A Molina, Olivia Szepietowski, Nozim Adxamovich Jumaev, Mehmet Kızılkaya, Octavio Viveiros, Carlos J. Toro‐Huamanchumo, Kenneth Yuh Yen Kok, Орал Оспанов, Syed Imran Abbas, Andrew Gerard Robertson, Yasser Fouad, Christos S. Mantzoros, Huijie Zhang, Nahúm Méndez‐Sánchez, Silvia Sookoian, Wah‐Kheong Chan, Sombat Treeprasertsuk, Leon A. Adams, Ponsiano Ocama, John Ryan, Nilanka Perera, Ala I. Sharara, Said A. Al‐Busafi, Christopher Opio, Manuel Garcia, Michelle Ching Lim-Loo, Elena Ruiz‐Úcar, Arun Prasad, Anna Casajoana, Tamer N. Abdelbaki, Ming‐Hua Zheng

Bibliographic record

VenueEClinicalMedicine · 2024
Typeerratum
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversity of Alberta HospitalUniversity of AlbertaUniversité de Montréal
Fundersnot available
KeywordsMedicineBody mass indexBurden of diseaseDisease burdenDiseaseEnvironmental healthGerontologyInternal medicine

Abstract

fetched live from OpenAlex

[This corrects the article DOI: 10.1016/j.eclinm.2024.102848.].

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)0.004
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.007
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.004

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 head0.061
GPT teacher head0.396
Teacher spread0.335 · 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 designObservational
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

Citations16
Published2024
Admission routes1
Has abstractyes

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