Long-term application of hydroxychloroquine could not prevent the infection of COVID-19
Bibliographic record
Abstract
INTRODUCTION: Current pandemic of the coronavirus induced disease 2019 (COVID-19) presents an urgent issue to the world due to lack of vaccine and medication. Hydroxychloroquine (HCQ) has generated a lot of controversies whether it is effective in prevention and treatment of COVID-19. Current report presents a 63-year-old woman who has taken HCQ for many years but still infected by COVID-19. CASE PRESENTATION: A patient with rheumatoid arthritis came to the clinic with fever and sore throat. The patient has been treated with 200 mg HCQ per day since 2016. Laboratory tests showed that the patient had lymphopenia, increased levels of high-sensitive C-reactive protein (hs-CRP) and serum Interleukin-6 (IL-6). Chest radiography showed that the patient had pneumonia. Throat swab test confirmed COVID-19 positive. On admission, she was treated with nebulized interferon alfa-2b, oral Lopinavir/Ritonavir, and ceftriaxone sodium for the COVID-19 in addition to HCQ. The patient stayed in hospital for 18 days, recovered from oxygen intake, and eventually discharged from hospital. Follow up investigation showed the patient developed antibody against COVID-19. CONCLUSIONS: Long-term application of HCQ could not prevent COVID-19 infection, but whether HCQ exerts benefit to alleviation of clinical symptoms and duration of hospital stays remains to be further investigated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".