The effect of corticosteroids, antibiotics, and anticoagulants on the development of post-COVID-19 syndrome in COVID-19 hospitalized patients
Bibliographic record
Abstract
Aim: To assess the effect of commonly used drugs in the treatment of hospitalized COVID-19 patients on the development of post-COVID-19 syndrome. Methods: Data from patients hospitalized in Medisch Spectrum Twente with an COVID-19 infection was collected from two separate databases, the MST clinical database containing the in-hospital electronic health records of COVID-19 patients and the Post-COVID cohort database containing patient follow-up data of the same patients. The aforementioned databases were then merged to determine the association between patient treatment with corticosteroids, antibiotics or anticoagulants during the hospital stay and the development of post-COVID-19 syndrome 6 months after hospital discharge. Results: A total of 123 patients had clinical data and 6 months follow-up data available. Out of these patients, 33 patients (26.8%) had developed and were still affected by post-COVID-19 syndrome 6 months after hospital discharge. Multivariate analysis showed that patients treated with corticosteroids were associated with a significantly lower chance (OR 0.32, 95% CI 0.11 to 0.90) of developing post-COVID-19 syndrome while antibiotics (OR 1.26, 95% CI 0.47 to 3.39) and anticoagulants (OR 0.55, 95% CI 0.18 to 1.71) were not significantly associated. Conclusion: This study showed that corticosteroids have a significant protective effect on the development of post-COVID-19 syndrome in hospitalized patients. While anticoagulants also indicate a protective trend, this effect was not statistically significant. On the contrary, patients treated with antibiotics were shown to have increased chances of developing post-COVID-19 syndrome, although this effect was also not statistically significant
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".