Coronavirus disease-related in-hospital mortality: a cohort study in a private healthcare network in Brazil
Bibliographic record
Abstract
COVID-19-related in-hospital mortality has been reported at 30.7-47.3% in Brazil, however studies assessing exclusively private hospitals are lacking. This is important because of significant differences existing between the Brazilian private and public healthcare systems. We aimed to determine the COVID-19-related in-hospital mortality and associated risk factors in a Brazilian private network from March/2020 to March/2021. Data were extracted from institutional database and analyzed using Cox regression model. Length of hospitalization and death-related factors were modeled based on available independent variables. In total, 38,937 COVID-19 patients were hospitalized of whom 3058 (7.8%) died. Admission to the intensive care unit occurred in 62.5% of cases, and 11.5% and 3.8% required mechanical ventilation (MV) and renal replacement therapy (RRT), respectively. In the adjusted model, age ≥ 61 years-old, comorbidities, and the need for MV and/or RRT were significantly associated with increased mortality (p < 0.05). Obesity and hypertension were associated with the need for MV and RRT (p < 0.05).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".