Therapy for secondary mitral regurgitation: time to ‘cut the chord’?
Bibliographic record
Abstract
Abstract Background There is an urgent need to assess the role of schools in the spread of SARS-CoV-2 in Canada to inform public health measures. We describe the epidemiology of SARS-CoV-2 infection in students and staff in the Vancouver Coastal Health (VCH) region in the first three months of the 2020/2021 academic year, and examine the extent of transmission in schools. Methods This descriptive epidemiologic study using contact tracing data included all SARS-CoV-2 cases reported to VCH between September 10 and December 18, 2020 who worked in or attended K-12 schools in-person. Case and cluster characteristics were described. Results There were 699 school staff and student cases during the study period, for an incidence of 55 cases per 10,000 population, compared to 73 per 10,000 population in all VCH residents. Among VCH resident staff and student cases, 53% were linked to a household case/cluster, <1.5% were hospitalized and there were no deaths. Out of 699 cases present at school, 26 clusters with school-based transmission resulted in 55 secondary cases. Staff members accounted for 54% of index cases (14/26) while comprising 14% of the school population. Among clusters, 88% had fewer than 4 secondary cases. Interpretation COVID-19 incidence in the school population was lower than that of the general population. There were no deaths and severe disease was rare. School-based transmissions of SARS-CoV-2 were uncommon and clusters were small. Our results support the growing body of evidence that schools do not play a major role in the spread of SARS-CoV-2.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".