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Record W2162759163 · doi:10.1017/s0950268806007771

Duration and distance of exposure are important predictors of transmission among community contacts of Ontario SARS cases

2007· article· en· W2162759163 on OpenAlexafffundabout
E Rea, J Laflèche, Shelley A. Stalker, B. K. GUARDA, Howard Shapiro, Ian Johnson, Susan J. Bondy, Ross Upshur, Marie Russell, Michael Eliasziw

Bibliographic record

VenueEpidemiology and Infection · 2007
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsUniversity of CalgaryProvincial Laboratory of Public HealthBarrie Urology GroupPublic Health OntarioUniversity of TorontoToronto Public Health
FundersOntario Ministry of Health and Long-Term Care
KeywordsMedicineAttack rateOutbreakConfidence intervalEpidemiologyDemographyPublic healthTransmission (telecommunications)Environmental healthEmergency medicineInternal medicineVirologyPathology

Abstract

fetched live from OpenAlex

We report attack rates and contact-related predictors among community contacts of severe acute respiratory syndrome (SARS) cases from the 2003 Toronto-area outbreak. Community contact data was extracted from public health records for single, well-defined exposures to a SARS case. In total, 8662 community-acquired exposures resulted in 61 probable cases; a crude attack rate of 0.70% [95% confidence interval (CI) 0.54-0.90]. Persons aged 55-69 years were at higher risk of acquiring SARS (1.14%) than those either younger (0.60%) or older (0.70%). In multivariable analysis exposures for at least 30 min at a distance of <or=1 m increased the likelihood of becoming a SARS case 20.4-fold (95% CI 11.8-35.1). Risk related to duration of illness in the source case at time of exposure was greatest for illness duration of 7-10 days (rate ratio 3.4, 95% CI 1.9-6.1). Longer and closer proximity exposures incurred the highest rate of disease. Separate measures of time and distance from source cases should be added to minimum datasets for the assessment of interventions for SARS and other emerging diseases.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.048
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.152
GPT teacher head0.381
Teacher spread0.228 · 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

Citations47
Published2007
Admission routes3
Has abstractyes

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