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Record W2768057291 · doi:10.1071/ma17064

Changing epidemiology of invasive meningococcal disease in Australia 1994–2016

2017· article· en· W2768057291 on OpenAlex
Helen Smith, Amy V. Jennison

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicrobiology Australia · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsEpidemiologyMedicineCase fatality rateOutbreakIncidence (geometry)DiseaseMeningitisMeningococcal diseaseVaccinationPublic healthPediatricsNeisseria meningitidisIntensive care medicineImmunologyInternal medicineVirologyPathologyBiology

Abstract

fetched live from OpenAlex

Invasive meningococcal disease (IMD) has a relatively low incidence in Australia, however remains a serious public health issue, with a case fatality rate of approximately 10% despite antimicrobial treatment. IMD is particularly seen in young children, but can affect all age groups. The disease has non-specific early symptoms, rapid clinical progression mainly manifesting as septicaemia and/or meningitis, and has the potential for long term sequelae in the survivors, including skin scarring, amputation, deafness and seizures. There are 13 serogroups, although most invasive infections worldwide are caused by serogroups A, B, C, W, and Y, with some recent outbreaks in Africa caused by serogroup X. The prevalent circulating serogroups can undergo dynamic shifts, generating dramatic changes in IMD epidemiology. Such serogroup shifts have important ramifications for vaccination programs and constant surveillance is crucial.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.074
GPT teacher head0.340
Teacher spread0.265 · 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