Coronavirus disease 2019 (COVID-19): latest developments in potential treatments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many viral respiratory infections can cause severe acute respiratory symptoms leading to mortality and morbidity. In the spring of 2003, the severe acute respiratory syndrome (SARS) outbreak caused by SARS-CoV spread globally. In the summer of 2012, the Middle East respiratory syndrome (MERS) outbreak caused by MERS-CoV occurred in Saudi Arabia. In the winter of 2019, the coronavirus disease 2019 (COVID-19) outbreak caused by a novel coronavirus SARS-CoV-2 occurred in China which rapidly spread worldwide causing a global pandemic. Up until 27 May 2020, there are 5.5 million confirmed cases of COVID-19 and 347,587 COVID-19 related deaths worldwide, and there has also been an unprecedented increase in socioeconomic and psychosocial issues related to COVID-19. This overview aims to review the current developments in preventive treatments and therapies for COVID-19. The development of vaccines for SARS-CoV-2 is ongoing and various clinical trials are currently underway around the world. It is hoped that existing antivirals including remdesivir and lopinavir-ritonavir might have roles in the treatment of COVID-19, but results from trials thus far have not been promising. COVID-19 causes a mild respiratory disease in the majority of cases, but in some cases, cytokine activation causes sepsis and acute respiratory distress syndrome, leading to morbidity and mortality. Immunomodulatory treatments and biologics are also being actively explored as therapeutics for COVID-19. On the other hand, the use of steroidal and nonsteroidal anti-inflammatory drugs (NSAIDs) has been discouraged based on concerns about their adverse effects. Over the past two decades, coronaviruses have caused major epidemics and outbreaks worldwide, whilst modern medicine has been playing catch-up all along.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it