COVID-19 Hospitalizations, ICU Admissions and Deaths Associated with the New Variants of Concern
Bibliographic record
Abstract
New variants of concern (VOCs) now account for 67% of all Ontario SARS-CoV-2 infections. Compared with early variants of SARS-CoV-2, VOCs are associated with a 63% increased risk of hospitalization, a 103% increased risk of intensive care unit (ICU) admission and a 56% increased risk of death due to COVID-19. VOCs are having a substantial impact on Ontario’s healthcare system. On March 28, 2021, the daily number of new SARS-CoV-2 infections in Ontario reached the daily number of cases observed near the height of the second wave, at the start of the province-wide lockdown, on December 26, 2020. The number of people hospitalized with COVID-19 is now 21% higher than at the start of the province-wide lockdown, while ICU occupancy is 28% higher (Figure 1). The percentage of COVID-19 patients in ICUs who are younger than 60 years is about 50% higher now than it was prior to the start of the province-wide lockdown. Because the increased risk of COVID-19 hospitalization, ICU admission and death with VOCs is most pronounced 14 to 28 days after diagnosis, there will be significant delays until the full burden to the health care system becomes apparent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".