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Record W3036683212 · doi:10.1016/j.cmet.2020.06.015

In-Hospital Use of Statins Is Associated with a Reduced Risk of Mortality among Individuals with COVID-19

2020· article· en· W3036683212 on OpenAlexafffund
Xiao-Jing Zhang, Juan‐Juan Qin, Xu Cheng, Lijun Shen, Yan-Ci Zhao, Yufeng Yuan, Fang Lei, Ming-Ming Chen, Huilin Yang, Liangjie Bai, Xiaohui Song, Lijin Lin, Xia Meng, Feng Zhou, Jianghua Zhou, Zhi‐Gang She, Lihua Zhu, Xinliang Ma, Qingbo Xu, Ping Ye, Guohua Chen, Li Liu, Weiming Mao, Youqin Yan, Bing Xiao, Zhigang Lu, Gang Peng, Mingyu Liu, Jun Yang, Luyu Yang, Changjiang Zhang, Haofeng Lu, Xigang Xia, Daihong Wang, Xiaofeng Liao, Wei Xiang, Bing-Hong Zhang, Xin Zhang, Juan Yang, Guang‐Nian Zhao, Peng Zhang, Peter P. Liu, Rohit Loomba, Yan‐Xiao Ji, Jiahong Xia, Yibin Wang, Jingjing Cai, Jiao Guo, Hongliang Li

Bibliographic record

VenueCell Metabolism · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsUniversity of Ottawa
FundersNational Key Research and Development Program of ChinaMajor Research PlanWuhan UniversityUniversity of TorontoNational Natural Science Foundation of ChinaFoundation for Innovative Research Groups of the National Natural Science Foundation of ChinaNational Science Foundation
KeywordsMedicineStatinHazard ratioPropensity score matchingCoronavirus disease 2019 (COVID-19)Proportional hazards modelInternal medicinePandemicRetrospective cohort studyMarginal structural modelRandomized controlled trialConfidence intervalDisease

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.067
GPT teacher head0.379
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 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

Citations501
Published2020
Admission routes2
Has abstractno

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