Impact of COVID-19 on hospitalization, death rate, and other inpatient measures among Asian patients in hospitals in California
Bibliographic record
Abstract
Objective: This study aims to analyze COVID-19 hospitalization and death rate in the Asian population of a predominantly Asian-serving multi-hospital system (ASMHS).Methods: The COVID-19 patient information was collected electronically from March 1 to November 12, 2020, including demographics, insurance, mortality, ICU admissions, and length of stay (LOS). Demographic characteristics were compared with the county-level and national data. A comparison of hospital LOS between Asians and non-Asians was conducted.Results: The prevalence ratio of deaths in Asians at ASMHS was 1.29, which was 53% higher than the county and 77% higher than the nation. The ICU admission for ASMHS Asian patients was 11.8% compared to 5.6% for non-Asian. Overall Asians and Asians aged > 65 had significantly longer LOS than non-Asians (p < .001).Conclusions: High prevalence ratio of deaths was noted in ASMHS’s Asian patients which may be related to older age, higher ICU rate, and longer LOS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".