Effectiveness of mass testing for control of COVID-19: a systematic review protocol
Bibliographic record
Abstract
Introduction Since March 2020, when the COVID-19 outbreak has been deemed a pandemic by the WHO, the SARS-CoV-2 spreading has been the focus of attention of scientists, authorities, public health agencies and communities around the world. One of the great concerns and challenges, mainly in low-income and middle-income countries, is the identification and monitoring of COVID-19 cases. The large-scale availability of testing is a fundamental aspect of COVID-19 control, but it is currently the biggest challenge faced by many countries around the world. We aimed to synthesise and critically evaluate the scientific evidence on the influence of the testing capacity for symptomatic individuals in the control of COVID-19. Methods and analysis A systematic review will be conducted in eight databases, such as Medical Literature Analysis and Retrieval System Online, ISI-of-Knowledge, Cochrane Central Register of Controlled Trials, Embase, SCOPUS, Latin American and Caribbean Health Sciences Literature, PsycINFO and Chinese National Knowledge Infrastructure, from inception to 30 July 2020. No restriction regarding the language, publication date or setting will be employed. Primary outcomes will include the sensitivity as well as the specificity of the tests for COVID-19. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Methodological assessment of the studies will be evaluated by the Cochrane Risk-of-Bias tool for randomised controlled trials, the MINORS for non-randomised studies and the Newcastle-Ottawa Scale for cohort or case–control studies. Findings will be structured according to the test type and target population characteristics and focused on the primary outcomes (sensitivity and specificity). Moreover, if sufficient data are available, a meta-analysis will be performed. Pooled standardised mean differences and 95% CIs will be calculated. Heterogeneity between the studies will be determined by I 2 statistics. Subgroup analyses will also be conducted. Publication bias will be assessed with funnel plots and Egger’s test. Heterogeneity will be explored by random effects analysis. Ethics and dissemination Ethical approval is not required. The results will be disseminated widely via peer-reviewed publication and presentations at conferences related to this field. PROSPERO registration number CRD42020182724.
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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 | 0.087 | 0.092 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.023 | 0.022 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.064 | 0.006 |
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".