Post COVID-19 complications, adjunct therapy explored, and steroidal after effects
Bibliographic record
Abstract
For COVID-19 survivors, defeating the virus is just the beginning of a long road to recovery. The inducibility and catastrophic effects of the virus are distributed across multiple organs. The induction of cytokine storms in COVID-19 patients is due to the interaction of the SARS-CoV-2 virus and the host receptor, leading to various immunopathological consequences that may eventually lead to death. So far, COVID-19 has affected tons of people across the world, but there is still no effective treatment. Patients facing complications of COVID-19 after recovery have shown extensive clinical symptoms similar to that of patients recovering from previously circulating coronaviruses. Previous knowledge and literature have opened up ways to treat this disease and manage post-COVID-19 complications, which pose a severe challenge to the health system globally and may exacerbate the fragmentation of diseases. The use of steroids as a treatment has resulted in various health problems and side-effects in COVID-19 patients. This review discusses various post-COVID-19 complications observed and adjunctive therapies used along with common COVID-19 treatment and spotlights their side effects and consequences. This review provides the latest literature on COVID-19, which emphasizes the subsequent complications in various organs, side effects of drugs, and alternative regimens used to treat COVID-19.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".