Bibliographic record
Abstract
BACKGROUND: Mortality statistics about daily deaths might change on weekends due to delays in reporting, uneven staffing, a different mix of personnel, or decreased efficiency. We hypothesized that reported deaths for COVID-19 might increase on weekends compared to weekdays. METHODS: We collected data from the World Health Organization COVID-19 database. All deaths from March 7, 2020 to March 7, 2022 were included (two years). The primary analysis evaluated mean daily deaths on weekends compared to the preceding five workdays. Analyses were replicated in ten individual countries: United States, United Kingdom, France, Germany, Italy, Spain, Russia, India, Brazil, and Canada. RESULTS: The mean COVID-19 daily deaths was higher on weekends compared to weekdays (8,532 vs. 8,083 p < 0.001), equal to a 6% relative increase (95% confidence interval 3% to 8%). The highest absolute increase was in the United States (1,483 vs. 1,220 deaths, p < 0.001). The second highest absolute increase was in Brazil (1,061 vs. 823 deaths, p < 0.001). The increase in deaths on weekends remained significant during the earlier and later months of the pandemic, as well as during the greater and lesser weeks of the pandemic. CONCLUSIONS: The apparent increased COVID-19 deaths reported on weekends might potentially reflect patient care, confound community trends, and affect the public perception of risk.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".