Rapid review protocol: Zinc for the prevention or treatment of COVID-19 and other coronavirus-related respiratory tract infections
Bibliographic record
Abstract
BACKGROUND: The global COVID-19 pandemic has prompted an urgent search for effective interventions. SARS-CoV-2 mortality/morbidity risk increases with age and for those chronic disease co-morbidities, both of which are associated with lower zinc status, as is the risk of infection. METHODS: Rapid review methods will be applied to a systematic review of zinc for the prevention or treatment of SARS-CoV-2 and viral respiratory tract infections in humans. Included are published studies reporting randomised and quasi-randomised controlled trials that compare zinc intervention to placebo and/or other comparator interventions. English and Chinese language databases will be searched for primary studies of viral respiratory tract infections and clinical trial registries for SARS-CoV-2 infections. Due to concerns about indirectness, studies evaluating non-SARS-CoV-2 coronavirus infections will be rated down by one level, and non-specific or confirmed non-coronavirus viral infections will be rated down by two levels. Review constraints include (1) using Google translate when screening articles published in languages other than English or Chinese and limited translation (2) following calibration, only one reviewer will screen articles, extract data, appraise quality and conduct the analysis, (3) prioritising data extraction and meta-analyses of SARS-CoV-2 studies and critical outcomes of other viral infections, followed by high risk groups and (4) reporting important preliminary findings prior to peer review if necessary. DISCUSSION: The application of these rapid review methods and broadening the inclusion criteria to include other coronavirus-related viral respiratory tract infections aims to enable a timely evidence appraisal of priority research questions and dissemination of results. STUDY REGISTRATION: PROSPERO CRD42020182044.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.102 | 0.255 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.023 | 0.016 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.201 | 0.047 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".