Sickle cell trait and the potential risk of severe coronavirus disease 2019—A mini‐review
Bibliographic record
Abstract
Coronavirus Disease 2019 (COVID-19) pandemic is a rapidly evolving public health problem. The severity of COVID-19 cases reported hitherto has varied greatly from asymptomatic to severe pneumonia and thromboembolism with subsequent mortality. An improved understanding of risk factors for adverse clinical outcomes may shed some light on novel personalized approaches to optimize clinical care in vulnerable populations. Emerging trends in the United States suggest possibly higher mortality rates of COVID-19 among African Americans, although detailed epidemiological study data is pending. Sickle cell disease (SCD) disproportionately affects Black/African Americans in the United States as well as forebearers from sub-Saharan Africa, the Western Hemisphere (South America, the Caribbean, and Central America), and some Mediterranean countries. The carrier frequency for SCD is high among African Americans. This article underscores the putative risks that may be associated with COVID-19 pneumonia in sickle cell trait as well as potential opportunities for individualized medical care in the burgeoning era of personalized medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".