SARS-COV-2 AND ARBOVIRUS INFECTIONS: PROTOCOL FOR A RAPID LIVING SYSTEMATIC REVIEW
Bibliographic record
Abstract
Context and objective: While cases of COVID-19 disease increase daily worldwide, outbreaks of arboviral infections have affected health systems of countries in tropical regions. The outcomes for patients and health systems of a possible syndemic are not clarified yet. Thus, we aim to systematically review the literature searching for evidence that describes the clinical presentation, severity and prognostic of SARS-CoV-2 and Arboviral coinfection. Design and setting: Protocol for a rapid living systematic review, that will follow the Cochrane Handbook for Systematic Reviews recommendations. We will include prospective and retrospective cohort, case-control studies and case series of patients with confirmed diagnosis of SARS-CoV-2 and Arboviral infection. We will perform the search strategy with no language restrictions on Medline via PubMed, Embase via Elsevier, Cochrane Library - Cochrane Central Register of Controlled Trials (CENTRAL), Portal Regional BVS - LILACS, Scopus and WebOfScience to identify published, ongoing, and unpublished studies. The selection and extraction will be performed by two authors. We will perform the critical appraisal of included studies with the Newcastle-Ottawa Scale and the certainty of evidence will be evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE).
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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 | 0.081 | 0.100 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.088 | 0.014 |
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".