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Record W3097669752 · doi:10.1101/2020.11.02.20220038

Benefits and risks of zinc for adults during covid-19: rapid systematic review and meta-analysis of randomised controlled trials

2020· preprint· en· W3097669752 on OpenAlexaff
Jennifer Hunter, Susan Arentz, Joshua Z. Goldenberg, Guoyan Yang, Jennifer Beardsley, Stephen P Myers, Dominik Mertz, Stephen Leeder

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsMcMaster UniversityHamilton Health SciencesMcMaster University Medical Centre
Fundersnot available
KeywordsMedicinePlaceboMeta-analysisRandomized controlled trialConfidence intervalAdverse effectInternal medicineMEDLINECoronavirus disease 2019 (COVID-19)Relative riskClinical trialAlternative medicinePathologyDisease

Abstract

fetched live from OpenAlex

ABSTRACT Objective To evaluate the benefits and risks of any type of zinc intervention to prevent or treat SARS-CoV-2. Design A living, systematic review and meta-analysis, incorporating rapid review methods. Data sources 17 English and Chinese databases and clinical trial registries were searched in April/May 2020, with additional covid-19 focused searches in June and August 2020. Eligibilitycriteria and analysis Randomized control trials (RCTs) published in any language comparing zinc to a control to prevent or treat SARS-CoV-2. Other viral respiratory tract infections (RTIs) were included, but the certainty of evidence downgraded twice for indirectness. Screening, data extraction, risk of bias appraisal (RoB-2 tool) and verification was performed by calibrated, single reviewers. RCTs with adult populations were prioritised for analysis. Results 123 RCTs were identified. None were specific to SARS-CoV-2 nor other coronaviruses. 28 RCTs evaluated oral (15-45mg daily), sublingual (45-300mg daily), or topical nasal (0.09-2.6 mg daily) zinc to prevent or treat nonspecific viral RTIs in 3,597 adults without zinc deficiency. Compared to placebo, zinc prevented 5 mild to moderate RTIs per 100 person-months, including in older adults (95% confidence interval 1 to 9) (number needed to treat (NTT)=20). There was no significant difference in the rates of non-serious adverse events (AE). For RTI treatment, a clinically important reduction in peak symptom severity scores was found for zinc compared to placebo (mean difference 1.2 points, 0.7 to 1.7), but not average daily symptom severity (standardised mean difference 0.2, 0.1 to 0.4). 19 fewer per 100 adults were at risk of remaining symptomatic over the first 7 days (2 to 38, NNT=5) and the mean duration of symptoms was 2 days shorter (0.2 to 3.5), however, there was substantial heterogeneity (I 2 = 82% and 97%). 14 more per 100 experienced a non-serious AE (4 to 16, NNT=7) such as nausea, or mouth or nasal irritation. No differences in illness duration nor AE were found when zinc was compared to active controls. No serious AE, including copper deficiency, were reported by any RCT. Quality of life outcomes were not assessed. Confidence in these findings for SARS-CoV-2 is very low due to serious indirectness and some concerns about bias for most outcomes. Conclusions Zinc is a potential therapeutic candidate for preventing and treating SARS-CoV-2, including older adults and adults without zinc deficiency (very low certainty). Zinc may also help to prevent other viral RTIs during the pandemic (moderate certainty) and reduce the severity and duration of symptoms (very low certainty). The pending results from seven RCTs evaluating zinc for SARS-CoV-2 will be tracked. Systematic review registration PROSPERO CRD42020182044

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)0.042
metaresearch head score (Gemma)0.096
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: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.096
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0280.042
Bibliometrics0.0120.009
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0030.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.249
GPT teacher head0.427
Teacher spread0.177 · 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
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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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