Bibliographic record
Abstract
The dreadful fear of the coronavirus disease 2019 (COVID-19), which is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with the deadly consequences, requires rapid development of pharmacological cures. The objective of this review is to speculate about possible pharmacological options, already available today to prevent or treat the COVID-19 in the early stage of its outbreak. A literature search across PubMed and internet was conducted. A number of studies dealing with COVID-19 were identified. The data elucidated that increased pro-inflammatory and decreased anti-inflammatory cytokines in combination with hypoxia, thromboembolism and pneumonia are involved in the pathogenesis of SARS-CoV-2 infection. Although many drugs has been tested in monotherapy regimen with varying outcome or without desirable effect, there is still hope for better results by simultaneously targeting the virus itself and its symptoms. Theoretically, a mixture of at least two available antiviral drugs in combination with other anti-pathogenic and immune system-enhancing drugs or combination of antiviral drugs with convalescent plasma seems likely to have much better effect than the monotherapy regimen of either of these drugs.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".