Evaluating the Clinical Outcomes of Remdesivir Among Patients Admitted With COVID-19 in a Tertiary Care Hospital
Post-publication record
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Bibliographic record
Abstract
INTRODUCTION: This study was conducted to determine whether remdesivir administration for treatment of coronavirus disease 2019 (COVID-19) is associated with reducing deaths among COVID-19 hospitalized patients. METHODOLOGY: It was a retrospective study, and the data was acquired at Ziauddin Hospital in Karachi, Pakistan. All patients admitted between February and May 2021 with severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection confirmed by polymerase chain reaction testing from nasopharyngeal samples were included in the study, including those who received at least five-day treatment of remdesivir and who did not receive even a single dose of remdesivir. RESULTS: Data of overall 174 patients were used, out of which 71 (40.80%) received remdesivir. After propensity score matching, 71 patients in the remdesivir group were successfully matched with the non-remdesivir patients on the basis of age, gender, and disease severity. Results of multivariable logistic regression showed that there is no significant difference in deaths between patients who received remdesivir and patients who did not receive remdesivir (p-value=0.122). However, the length of hospital stay was significantly lower in the remdesivir group than in the control group (p-value=0.001). CONCLUSION: Results of this study can provide evidence that remdesivir can be efficient in reducing the duration of COVID-19 illness, and a five-day course of treatment is sufficient for patients to get clinical benefits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".