Invasive meningococcal disease--improving management through structured review of cases in the Hunter New England area, Australia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".