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Record W2516833349 · doi:10.14745/ccdr.v42i04a01

Invasive bacterial diseases in Northern Canada, 2006–2013

2016· article· en· W2516833349 on OpenAlexafffundvenueabout
YA Li, Irene Martín, RSW Tsang, SG Squires, Walter Demczuk, Saaketh Desai

Bibliographic record

VenueCanada Communicable Disease Report · 2016
Typearticle
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsIncidence (geometry)MedicineSerotypeEpidemiologyPneumococcal diseasePopulationDiseaseHerd immunityStreptococcus pneumoniaeImmunologyInternal medicineBiologyEnvironmental healthMicrobiologyAntibiotics

Abstract

fetched live from OpenAlex

BACKGROUND: Northern populations were at a high risk of developing invasive bacterial diseases (IBDs). Since the last published study that described IBDs in Northern Canada, a number of vaccines against some bacterial pathogens have been introduced into the routine childhood immunization schedule. OBJECTIVE: To describe the epidemiology of IBDs in Northern Canada from 2006 to 2013. METHODS: disease (Hi), invasive Group A streptococcal disease (iGAS), invasive meningococcal disease (IMD) and invasive Group B streptococcal disease (GBS)) were extracted from the International Circumpolar Surveillance (ICS) program and the Canadian Notifiable Diseases Surveillance System. Incidence rates were calculated per 100,000 population per year. RESULTS: =0.0004) over the study period. Among Hi cases, 69.8% were Hia and 71.6% of these were in children under than 5 years. Of 13 IMD cases, 8 were serogroup B and 2 of them died. CONCLUSION: Northern population in Canada, especially infants and seniors among First Nations and Inuit, are at a high risk of IPD, Hi and iGAS. Hia is the predominant serotype in Northern Canada.

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 score0.268
Threshold uncertainty score0.990

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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

Citations18
Published2016
Admission routes4
Has abstractyes

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