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Record W3198613071 · doi:10.1016/j.eclinm.2021.101111

Mental and neurological disorders and risk of COVID-19 susceptibility, illness severity and mortality: A systematic review, meta-analysis and call for action

2021· article· en· W3198613071 on OpenAlexaff
Lin Liu, Shuyu Ni, Wei Yan, Qing-Dong Lu, Yimiao Zhao, Yingying Xu, Huan Mei, Le Shi, Kai Yuan, Ying Han, Jiahui Deng, Yankun Sun, Shiqiu Meng, Zhengdong Jiang, Na Zeng, Jianyu Que, Yongbo Zheng, Bei-Ni Yang, Yi-Miao Gong, Arun Ravindran, Thomas R. Kosten, Yun Kwok Wing, Xiangdong Tang, Junliang Yuan, Ping Wu, Jie Shi, Yanping Bao, Lin Lü

Bibliographic record

VenueEClinicalMedicine · 2021
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsUniversity of Toronto
FundersPeking University Health Science CenterFundamental Research Funds for the Central UniversitiesNational Institute of Environmental Health SciencesPeking UniversityNational Key Research and Development Program of ChinaBijzonder Onderzoeksfonds UGentNational Natural Science Foundation of China
KeywordsMedicineMeta-analysisOdds ratioCochrane LibraryPsycINFOObservational studyMental illnessConfidence intervalMEDLINECohort studyPsychiatrySubgroup analysisInternal medicineMental health

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.034
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.034
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.079
GPT teacher head0.420
Teacher spread0.341 · 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 designMeta-analysis
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

Citations138
Published2021
Admission routes1
Has abstractyes

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