Dexamethasone chronotherapy of COVID-19 patients admitted to intensive care unit: an exploratory study
Bibliographic record
Abstract
BACKGROUND: Dexamethasone has been demonstrated to be a potential treatment approach preventing COVID-19 related fatalities by managing the cytokine release storm (CRS). The expression of the inflammatory mediators involved in the CRS is regulated by the circadian biology of the immune response and it peaks during the evening. Accordingly, it has been hypothesized that the administration of anti-inflammatory medications in the evening could help better manage the CRS. Therefore, we investigated the association between dexamethasone administration time and COVID-19 mortality. METHODS: A retrospective cohort study was conducted using electronic health records of COVID-19 patients hospitalized in the State of Qatar between March 2020 and April 2021. The exposure group received dexamethasone between 16:00 h and 04:00 h, while the control group received dexamethasone between 04:00 h and 16:00 h. RESULTS: From the 875 COVID-19 patients included in the study, 161 received dexamethasone treatments between 16:00 h and 04:00 h while 714 received it between 04:00 h and 16:00 h. After adjusting for confounding variables, dexamethasone given between 16:00 h and 04:00 h was associated with lower odds of COVID-19 mortality (OR: 0.22, CI 95%: 0.06, 0.84). CONCLUSION: Dexamethasone administration tailored to the circadian rhythm was associated with lower odds of mortality in hospitalized COVID-19 patients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".