Transmission of SARS-CoV-2 associated with cruise ship travel: protocol for a systematic review (Version 1)
Bibliographic record
Abstract
Abstract Background Maritime and river travel, including cruise ships, have been implicated with spreading viruses through infected passengers and crew. Given the novelty of the SARS-CoV-2 infection, early cruise ship travel transmission models of spread are based on what is known of the dynamics of other respiratory viral infections. Our objective is to provide a rapid summary and evaluation of relevant data on SARS-CoV-2 transmission aboard cruise ships, report policy implications, and highlight research gaps requiring attention. Methods We will search LitCovid, medRxiv, Google Scholar, and the WHO Covid-19 database using COVID-19, SARS-CoV-2, transmission, and cruise ship appropriate synonyms. We will also search the reference lists of included studies for additional relevant studies. We will include studies reporting onboard SARS-CoV-2 transmission from passengers and/or crew to passengers and/or crew. We will consider any potential transmission mode. We will assess study quality based on five criteria and report important findings. The outcome will consist of the onboard cruise ships’ transmission of SARS-CoV-2. We will provide a narrative summary of the data and report the outcomes, including quantitative estimates where feasible and relevant. Where possible, compatible datasets may be pooled for meta-analysis. Expected results We will present the evidence in three distinct packages: study description, methodological quality assessment and data extracted. We will summarize the evidence and will draw 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 | 0.061 | 0.135 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.125 | 0.015 |
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".