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Record W2992513171 · doi:10.1017/ice.2019.334

Are healthcare personnel at higher risk of seasonal influenza than other working adults?

2019· article· en· W2992513171 on OpenAlexaffabout
Brenda L. Coleman, Stefan P. Kuster, Kevin Katz, Mark Loeb, Shelly McNeil, Matthew Muller, Jeff Powis, Andrew E. Simor, Kristy Coleman, Todd F. Hatchette, Allison McGeer

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2019
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsHealth Sciences CentreSt. Michael's HospitalSunnybrook Health Science CentreSinai Health SystemNorth York General HospitalToronto East General HospitalUniversity of TorontoDalhousie UniversityParkwood InstituteMcMaster University
Fundersnot available
KeywordsMedicineOdds ratioOddsConfidence intervalHealth careProspective cohort studyYoung adultCohort studyInternal medicineEmergency medicineLogistic regression

Abstract

fetched live from OpenAlex

Abstract Background: Adults are at risk of being exposed to influenza from many sources. Healthcare personnel (HCP) have the additional risk of being exposed to ill patients. Objective: To determine whether HCP were at higher risk than adults working in nonhealthcare roles (non-HCP). Design: Prospective cohort study. Setting: Acute-care hospitals and other businesses in Toronto, Ontario, Canada. Methods: Adults aged 18–69 years were enrolled for 1 or more of the 2010/2011, 2011/2012, and 2012/2013 influenza seasons. Swabs collected during acute respiratory illnesses were tested for influenza and pre- and postseason blood samples were tested for influenza-specific immune response. Results: The adjusted odds of influenza were similar for HCP and non-HCP (odds ratio [OR], 1.29; 95% confidence interval [CI], 0.63–2.63). Older adults and those vaccinated against influenza had lower odds, and those who shared their workspace and who used corrective eyewear had higher odds of influenza. Conclusions: HCP and other working adults are at similar risk of influenza infection.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.060
GPT teacher head0.359
Teacher spread0.299 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2019
Admission routes2
Has abstractyes

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