Effect of methylprednisolone in severe and critical COVID-19: Analysis of 102 cases
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak has brought great challenges to public health. Aggravation of COVID-19 is closely related to the secondary systemic inflammatory response. Glucocorticoids are used to control severe diseases caused by the cytokine storm, owing to their anti-inflammatory effects. However, glucocorticoids are a double-edged sword, as the use of large doses has the potential risk of secondary infection and long-term serious complications, and may prolong virus clearance time. Nonetheless, the risks and benefits of glucocorticoid adjuvant therapy for COVID-19 are inconclusive. AIM: To determine the effect of methylprednisolone in severe and critically ill patients with COVID-19. METHODS: ), and coagulation function. Patient clinical outcomes were discharge or death. RESULTS: = 0.655). The COX regression equation was used to correct the variables with differences, and the results showed that methylprednisolone treatment did not improve prognosis. CONCLUSION: Methylprednisolone treatment does not improve prognosis in severe and critical COVID-19 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".