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Record W2620603451 · doi:10.1155/2017/9854103

Evaluating the Timeliness of Enteric Disease Surveillance in British Columbia, Canada, 2012-13

2017· article· en· W2620603451 on OpenAlexafffundabout
Eleni Galanis, Marsha Taylor, Kamila Romanowski, Olga Bitzikos, Jennifer Jeyes, Craig Nowakowski, Jason Stone, Michelle Murti, Ana Paccagnella, Sara Forsting, Sophie Li, Linda Hoang

Bibliographic record

VenueCanadian Journal of Infectious Diseases and Medical Microbiology · 2017
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsFraser HealthUniversity of British ColumbiaBC Centre for Disease ControlIsland HealthVancouver Coastal HealthInterior Health
FundersBritish Columbia Centre for Disease Control
KeywordsOutbreakMedicineDisease surveillanceSalmonellaDisease controlEnvironmental healthShigellaSubtypingListeriaDiseaseListeria monocytogenesInternal medicinePathologyComputer scienceBiology

Abstract

fetched live from OpenAlex

Timely surveillance of enteric diseases is necessary to identify and control cases and outbreaks. Our objective was to evaluate the timeliness of enteric disease surveillance in British Columbia, Canada, compare these results to other settings, and recommend improvements. In 2012 and 2013, information was collected from case report forms and laboratory information systems on 2615 Salmonella , shigatoxin-producing E. coli , Shigella , and Listeria infections. Twelve date variables representing the surveillance process from onset of symptoms to case interview and final laboratory results were collected, and intervals were measured. The median time from onset of symptoms to reporting subtyping results to BC epidemiologists was 26–36 days and from onset of symptoms to case interview was 12–14 days. Our findings were comparable to the international literature except for a longer time (up to 29 day difference) to reporting of PFGE results to epidemiologists in BC. Such a delay may impact our ability to identify and solve outbreaks. Several process and system changes were implemented which should improve the timeliness of enteric disease surveillance.

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)0.005
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 score0.094
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.273
Teacher spread0.262 · 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

Citations8
Published2017
Admission routes3
Has abstractyes

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