Early Warning and Rapid Public Health Response to Prevent COVID-19 Outbreaks in Long-term Care Facilities (LTCF) by Monitoring SARS-CoV-2 RNA in LTCF Site-specific Sewage Samples and Assessment of Antibodies Response in this Population [SSSIS, study data contributed to the CITF Databank]
Bibliographic record
Abstract
Background:Residents of Canadian long-term care facilities (LTCF) experience challenges with social distancing and the movement of staff between the facility and their communities, which increases their vulnerability to COVID-19. Aims of the CITF-funded study: The SSSIS study aimed to 1) investigate and conduct a cost-benefit analysis on the use of a site-specific sewage surveillance of SARS-CoV-2 as an early warning system to prevent outbreaks in LTCFs; 2) study the antibody response of residents and staff in LTCFs; and 3) evaluate the execution of commercial assays aimed to detect vaccine-induced antibodies and the correlation with neutralizing antibody responses. Methods: For Part I of this study where the unit of analysis was long-term care facilities, sewage samples were monitored semiweekly: if the sample tested negative for SARS-CoV-2, no action was taken, but positive results warranted an investigation if necessary. Part II was a cohort study was conducted in a different set of LTCF, where residents and staff were recruited from the LTCF Associations across Edmonton region of Alberta. Participants completed a questionnaire and provided a blood sample (venipuncture or DBS) at baseline and at follow-ups pre-vaccine, pre-second dose, and 3-, 6-, 12-, and 18-months post second dose. Summary of dataset contents: For Part I of this study, a total of 2,936 sewage samples were collected from 12 sites between Jan 2021 and May 2023: 36% (1,058) of the sewage samples were tested positive for SARS-CoV-2. For Part II of this study, the datasets include 364 participants (241 staffs and 123 residents) who completed a questionnaire between Feb 2021 and Aug 2021. All participants provided one or more blood samples at baseline and during follow-up visits up to Dec 2022. A total of 966 blood samples were collected. Variables include data in the following areas of information: demographics (age, gender), exposure risk factors (dining place for residence, working site for staff), longitudinal follow-up for COVID infections (COVID test) and vaccination, and serology.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".