Uncertainty around the Long-Term Implications of COVID-19
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 231 million people globally, with more than 4.7 million deaths recorded by the World Health Organization as of 26 September 2021. In response to the pandemic, some countries (New Zealand, Vietnam, Taiwan, South Korea and others) have pursued suppression strategies, so-called Zero COVID policies, to drive and maintain infection rates as close to zero as possible and respond aggressively to new cases. In comparison, European countries and North America have adopted mitigation strategies (of varying intensity and effectiveness) that aim primarily to prevent health systems from being overwhelmed. With recent advances in our understanding of SARS-CoV-2 and its biology, and the increasing recognition there is more to COVID-19 beyond the acute infection, we offer a perspective on some of the long-term risks of mutational escape, viral persistence, reinfection, immune dysregulation and neurological and multi-system complications (Long COVID).
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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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".