Management of Hemoglobin Disorders During the COVID-19 Pandemic
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) is an emerging infectious disease that has become a global public health concern after being first reported in China and has subsequently spread worldwide. It causes mild to severe respiratory illness with some flu-like symptoms. The causal virus behind this disease, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), conceivably attacks the receptors of the respiratory system of the human body but has no strict evidence of attacking the blood cells yet. However, patients with hemoglobin disorders (e.g., sickle cell anemia, thalassemia) are vulnerable to this global health situation due to their clinical complications. Such patients are generally more prone to viral and bacterial infections, which can worsen their physical condition. Some of these patients present immunocompromised conditions, e.g., splenectomized or post-transplant patients. Therefore, they should follow some preventive steps such as shielding as well as the general guidelines for the COVID-19 pandemic. Transfusion dependent patients require regular monitoring for iron overload, and iron chelation therapy may be stopped by the physician depending on the situation. This article reviews the management strategies and provides some crucial recommendations for people in the corner with hemoglobin disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".