Introduction to Novel Coronavirus 19 (COVID-19), Its Impact and Treatments under Investigation
Bibliographic record
Abstract
Aims: This study aimed to provide more information about the influence of Coronavirus Disease2019 (COVID-19) on infected individuals.The symptoms, conditions, and treatments used may be served as important clues to find out potential medications.Materials & Methods: Various current papers were reviewed, and the findings were summarized.In addition, other diseases such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which have similar causes or symptoms in patients, were investigated.Findings: The most common symptoms in infected patients were fever (98%), dry cough (76%), and dyspnoea (55%).Mechanical ventilation was the main supportive treatment for ICU patients, and the mortality rate of patients with chronic diseases in the intensive care unit (ICU) was high (55%).The virus is highly contagious compared to the previous Betacoronaviruses causing epidemic, but its mortality rate is lower so that most of the infected patients studied had minor symptoms or were asymptomatic.Several treatments, such as antiviral agents and antimalarial drugs, are presently being proposed and tested, but none have yet been proven to be effective.Conclusions: Seniors and patients with chronic diseases are at higher risk of COVID-19 induced severe consequences and mortality.Currently, supportive treatment is the mainstay for severely ill patients.
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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".