The evidence on transmission dynamics of COVID-19 from pre- and asymptomatic cases: protocol for a systematic review (Version 2)
Bibliographic record
Abstract
Abstract Background The role of forward transmission of infection from cases of SARS-CoV-2 who remain without symptoms and signs throughout the active phase of the disease (asymptomatic) and those who have not developed symptoms or signs when surveyed (presymptomatic) is at present unclear, despite the important role that they may play in transmission dynamics. Methods We will search LitCovid, medRxiv, Google Scholar and the WHO Covid-19 database using Covid-19, SARS-CoV-2, transmission, and appropriate synonyms as search terms. We will also search the reference lists of included studies for additional relevant studies. We will include studies of people exposed to SARS-CoV-2 within 2-14 days of close contact or suspected community or institutional exposure to index asymptomatic or pre-symptomatic infected individuals, as defined in each study along with secondary case(s) infected. We will include only studies that provide proof of transmission outcome using culturable virus and /or genetic sequencing. The inclusion of this higher-quality evidence aims to overcome the methodological shortcomings of lower quality studies. We will assess the microbiologic or genetic sequencing evidence in an effort to inform the quality of the chain of transmission evidence and adequacy of follow up of sign and symptom monitoring. Expected results We intend to present the evidence in three distinct packages: study description, methodological quality assessment and data extracted. We intend on summarising the evidence and drawing conclusions as to the quality of the evidence.
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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 | 0.103 | 0.166 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.021 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.087 | 0.011 |
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".