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Record W2043444450 · doi:10.1186/1753-6561-5-s6-o79

Swift mobilization of infection control, employee health, clinicians, engineering, laboratory and public health averted secondary cases following a large measles exposure at the British Columbia Children’s Hospital, Vancouver, BC, Canada

2011· article· en· W2043444450 on OpenAlexaffabout
Ebony S. Thomas, Simon Dobson, Ghada N. Al‐Rawahi, Larry Holmes, Réka Gustafson, S Papilla, Linda Hoang, Peter Tilley

Bibliographic record

VenueBMC Proceedings · 2011
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsVancouver Coastal HealthProvincial Health Services AuthorityBC Children's Hospital
Fundersnot available
KeywordsMeaslesMedicineSwiftPublic healthMobilizationInfection controlEnvironmental healthGerontologyFamily medicinePediatricsPolitical scienceNursingVaccinationImmunologySurgery

Abstract

fetched live from OpenAlex

A massive collaborative effort ensued, involving numerous hospital departments, to identify/ensure control of potentially infected individuals. Patients: 52 patients were exposed: 16, who were immunocompromised, received prophylactic Immune Globulin, 21 had received 2 MMR doses, 3 had 1 dose, 6 were seropositive and 6 patients’ immunity remained unknown. Staff: 221 staff members were exposed; 103 were immune, 118 had unknown immunity [17 produced immunization record; 22 remained unknown; 79 were tested (71/79 seropositive, 6 susceptible, 2 “equivocal”)]. 21 staff members were furloughed from work, representing 231 worker-days. None of those exposed developed measles. This exposure illustrates the importance of early clinical recognition, rapid laboratory testing, measles risk awareness and strict adherence to airborne isolation protocols. An ongoing education program was created to ensure proper clinical management and infection control measures for all patients with rashes. A tight maintenance program for ventilation monitoring and a database for managers to access staff immunity records were also implemented.

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)0.000
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 score0.420
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.225
Teacher spread0.212 · 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

Citations0
Published2011
Admission routes2
Has abstractyes

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