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Record W3188358906 · doi:10.1177/20543581211036213

Province-Wide Prevalence Testing for SARS-CoV-2 of In-Center Hemodialysis Patients and Staff in Ontario, Canada: A Cross-Sectional Study

2021· article· en· W3188358906 on OpenAlexaffabout
Daphne Sniekers, James K. H. Jung, Peter G. Blake, Rebecca Cooper, Jerome A. Leis, Matthew Muller, Vlad Padure, Philip Holm, Angie Yeung, Leena Taji, Phil McFarlane, Matthew J. Oliver

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsHumber River Regional HospitalWestern UniversitySt. Michael's HospitalOntario Stroke NetworkSunnybrook Health Science CentreHealth Sciences Centre
Fundersnot available
KeywordsMedicineHemodialysisAsymptomaticDialysisCross-sectional studyPandemicPopulationCoronavirus disease 2019 (COVID-19)Emergency medicineInternal medicinePediatricsDiseaseEnvironmental healthInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

BACKGROUND: People receiving in-center hemodialysis face a high risk for contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and experience poor outcomes. During the first wave of the coronavirus disease 2019 (COVID-19) pandemic in Ontario (between March and June 2020), it was unclear whether asymptomatic or presymptomatic cases were common and whether widespread testing of all dialysis patients and staff would identify cases earlier and prevent transmission. Ontario has a population of about 14.5 million. Approximately 8900 people receive dialysis across 102 in-center dialysis units. OBJECTIVE: The objective of this study was to determine participation rates for patients and staff in point prevalence testing in dialysis units across the province and to determine the prevalence of asymptomatic or presymptomatic infection. DESIGN: Cross-sectional study design. SETTING: In-center hemodialysis units at 27 renal programs across Ontario. PARTICIPANTS: Patients and staff in in-center dialysis units in Ontario. MEASUREMENTS: Participation rates, demographic data, SARS-CoV-2 positivity rates, and COVID-19-related symptom data. METHODS: From June 8 to 30, 2020, all in-center dialysis patients and staff in the Province of Ontario were requested to undergo a symptom screening assessment and nasopharyngeal swab. Testing was done using polymerase chain reaction to detect SARS-CoV-2. A standardized questionnaire of atypical and typical COVID-19-related symptoms was administered to patients, to assess for new or worsening COVID-19-related symptoms. RESULTS: Patient participation was 83% (7155 of 8612) of which 15 tests were positive: less than 5 (<0.07%) were new positive cases, 7 were false positive, and the remaining were recovered positives. Half of the new positive cases had symptoms. Common symptoms reported included fatigue (4%), falls (4%), runny nose (3%), dyspnea (3%), and cough (3%). Staff participation was 49% (2109 of 4325), and less than 5 (<0.24%) were asymptomatic positive. LIMITATIONS: As point prevalence testing was voluntary, not all patients and staff participated. Lower participation rate may be due to decreasing new cases in Ontario, and testing or pandemic fatigue, among other factors. This study did not use serology to identify prior infections because it was not widely available in Ontario. With respect to the standardized symptom questionnaire, it was only available in English and French and could not be tested due to the urgency of the initiative. CONCLUSIONS: Participation among patients in point prevalence testing was good, but participation among staff was relatively low. Asymptomatic positivity in the dialysis patient and staff population was rare during the first wave of the COVID-19 pandemic in Ontario.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.303
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2021
Admission routes2
Has abstractyes

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