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Record W2133877774 · doi:10.1093/pubmed/fdp075

Invasive meningococcal disease--improving management through structured review of cases in the Hunter New England area, Australia

2009· article· en· W2133877774 on OpenAlexaff
Chantal Guímont, Carolyn Hullick, David N Dürrheim, Nick Ryan, John Ferguson, Peter Massey

Bibliographic record

VenueJournal of Public Health · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsCentre hospitalier de l'Université Laval
Fundersnot available
KeywordsMedicineAuditPublic healthDiseaseEpidemiologyMeningococcal diseaseCause of deathInfectious disease (medical specialty)PediatricsEmergency medicineIntensive care medicineMedical emergencyInternal medicineNeisseria meningitidisPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Invasive meningococcal disease (IMD) is the most common infectious cause of death in childhood in developed countries. This disease may cause severe disability or death if a patient is sub-optimally managed. An audit was performed in Australia of all 2005-06 notified IMD cases to elicit correctable issues. METHODS: Over the 2 year period, 24 cases were notified in the Hunter New England Health area. These cases were reviewed by an expert panel to highlight key correctable issues in recognition and management of IMD. RESULTS: The 24 patients were aged between 1 month and 70 years. Thirteen (54%) were children and 14 (58%) were women. Six (25%) cases developed complications, two being severe (one death, one limb amputations). These patients had risk factors for IMD. The emergency department average delay between assessment and administration of antibiotics was 57.8 min. CONCLUSION: There were avoidable factors identified in both patients with a poor outcome. Length of delay in initiating antibiotic therapy has been associated with poor outcome, thus the delay in our series is of concern. The audit highlighted many potentially correctable issues in the medical, laboratory and public health management of IMD cases.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.091
GPT teacher head0.346
Teacher spread0.254 · 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 designNot applicable
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

Citations9
Published2009
Admission routes1
Has abstractyes

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