Dealing with coronavirus disease 2019 (COVID-19) outbreaks in long-term care homes: A protocol for room moving and cohorting
Bibliographic record
Abstract
present with a range of both typical and/or atypical symptoms, outside those included the current case definition for COVID-19 [1][2][3]7 Additionally, in this study, some individuals developed symptoms up to 1 week after they tested positive for SARS-CoV-2.During this time, individuals may have the potential to transmit the virus unknowingly to others, which may have devastating impacts in high-risk settings such as care homes.Therefore, it is critical that all residents and staff are tested in an outbreak situation to identify COVID-19 asymptomatic and presymptomatic individuals who could transmit SARS-CoV-2 before significant symptoms develop.Once it is known that someone has the infection, particularly in a care-home setting, strict infection control measures are required to contain the spread of infection.During this study, once an outbreak had been confirmed, this was managed in line with the existing public health guidance for outbreaks in a care home.In conclusion, the findings of this study emphasize the need to identify residents and staff with atypical symptoms and to identify asymptomatic residents and staff through comprehensive and regular screening for SARS-CoV-2.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.019 |
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".