SARS-CoV-2 Exposures at a Large Gathering Event and Acquisition of COVID-19 in the Post-Vaccination Era: A Randomized Trial Is Possible During the Pandemic
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has had a major impact on all facets of life, including outcomes that were very significant to the health of the general public but also deleterious to the economy, culture, politics, social cohesion, food security, travel, human rights, education, and access to accurate information [1, 2]. The response to the COVID-19 pandemic was hampered by tensions between the dichotomous perspectives of public health and the acceptance (or lack thereof) of social measures intended to curb transmission, such as lockdowns, school closures, mask mandates, and curfews. Among other unintended consequences, these measures exposed considerable conflict, in part fed by a variety of opinions that emerged from the lack of clear scientific evidence. It is widely accepted that randomized, controlled trials (RCTs) provide the least biased evidence when testing interventions [3]. Randomization provides balanced groups of participants with respect to known and unknown bias, whereas observational studies are prone to confounding and cannot address unknown confounders. RCTs of pharmaceutical interventions including antivirals and vaccines were designed, funded, and deployed at an unprecedented pace during the COVID-19 pandemic. However, the same expediency was not seen for RCTs for nonpharmaceutical interventions (NPIs). For reasons that are not well understood, RCTs failed to be designed and implemented for some of the most disruptive policies applied to address COVID-19, a situation that has been described as a “pandemic tragedy” [4, 5]. Some have suggested that RCTs in a pandemic are too difficult or impossible to conduct and that mechanistic or observational evidence is sufficient [6]. Unfortunately, pursuing this type of evidence, to the exclusion of knowledge derived from RCTs, will not provide the best information that is essential to guide public health decisions during a pandemic.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".