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Record W3135935417 · doi:10.1111/dom.14382

The J‐shaped relationship between body mass index and mortality in patients with <scp>COVID</scp> ‐19: A dose‐response meta‐analysis

2021· review· en· W3135935417 on OpenAlexaboutno aff
Huei‐Kai Huang, Khulood Bukhari, Carol Chiung‐Hui Peng, Duan‐Pei Hung, Ming‐Chieh Shih, Rachel Huai‐En Chang, Shu‐Man Lin, Kashif M. Munir, Yu‐Kang Tu

Bibliographic record

VenueDiabetes Obesity and Metabolism · 2021
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFunnel plotBody mass indexCoronavirus disease 2019 (COVID-19)Meta-analysisPublication biasDemographyIndex (typography)StatisticsPlot (graphics)MedicineMathematicsInternal medicineComputer scienceSociology

Abstract

fetched live from OpenAlex

The peer review history for this article is available at https://publons.com/publon/10.1111/dom.14382. The data that support the findings of this study are available from the corresponding author upon reasonable request. Table S1. Quality assessment of the included cohort studies using the Newcastle-Ottawa Scale Figure S1. PRISMA flow diagram of the literature search and article selection Figure S2. Forest plot of the relative risk of mortality for highest versus lowest categories of body mass index in COVID-19 patients Figure S3. Funnel plot to assess publication bias in studies comparing mortality between highest versus lowest categories of body mass index in COVID-19 patients Figure S4. The leave-one-out sensitivity analysis of the relative risk of mortality for highest versus lowest categories of body mass index in COVID-19 patients with each study omitted individually one at a time Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.059
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.262
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.059
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.095
GPT teacher head0.407
Teacher spread0.312 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreReview

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

Citations94
Published2021
Admission routes1
Has abstractyes

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