High mortality and morbidity among vaccinated residents infected with the SARS-CoV-2 Omicron variant during an outbreak in a nursing home in Kyoto City, Japan
Bibliographic record
Abstract
BACKGROUND: Outbreaks of coronavirus disease 2019 (COVID-19) in long-term care facilities are associated with mortality, although vaccination have contributed to improvements. This study reports clinical impacts of a COVID-19 outbreak in a nursing home for elderly individuals in Kyoto City, Japan. METHODS: We performed epidemiologic and molecular investigations of the outbreak and characterized outcomes of the nursing home residents. RESULTS: During the outbreak period, a total of 31 residents (39.2%) and 26 staff members (49.1%) were infected with COVID-19. All residents and staff received two doses of a vaccine approximately 7 months prior. Ten residents with severe hypoxemia could not be transferred to a hospital due to a shortage of beds for COVID-19 patients. Within 90 days of the onset of the outbreak, 8 residents with COVID-19 (25.8%) died. A total of 48.4% of residents with COVID-19 developed 1 or more comorbidities. Viral genome analysis showed that the outbreak was caused by the Omicron BA.1.1.2 variant. CONCLUSIONS: Despite vaccination, high mortality and morbidity were observed in the COVID-19 outbreak due to the Omicron variant. Limiting medical care for residents with COVID-19 in facilities that experience ongoing outbreaks may be needed to reduce the risk of mortality among nursing home residents.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".