Changes in the clinical and epidemiological features of group A streptococcal bacteraemia in Australia's Northern Territory
Bibliographic record
Abstract
Objective: Invasive group A streptococcus (iGAS) disease is an important cause of mortality globally. The incidence of iGAS in Australia’s tropical Northern Territory (NT) has been previously reported as 32.2/100 000 in Indigenous people for the period 1991–1996. We aimed to measure the incidence and severity of iGAS disease in the NT since this time. Methods: We collected demographic data for all GAS blood culture isolates over a 12-year period (1998–2009) from the three hospital laboratories serving the tropical NT. We then collected detailed clinical information from hospital records and databases for the subset of these patients who were admitted to Royal Darwin Hospital during 2005–2009. Results: There were 295 confirmed cases of GAS bacteraemia over the study period, with a mean (SD) age of 42.1 (22.0) years, and 163 (55.0%) were male. The annual age-adjusted incidence was 15.2 (95% CI 13.4–16.9)/100 000 overall and 59.4 (95% CI 51.2–67.6) in Indigenous Australians. For 2005–2009, there were 123 cases with the most common focus of infection being skin/soft tissue [44 (35.6%)]; 29 patients (23.6%) required intensive care unit admission and 20 (16.3%) had streptococcal toxic shock syndrome. Antecedent sore throat or use of non-steroidal anti-inflammatory drugs was rare, but current or recent scabies, pyoderma and trauma were common. Conclusion: The incidence and severity of iGAS are high and increasing in tropical northern Australia, and urgent attention is needed to improve surveillance and the social determinants of health in this population. This study adds to emerging data suggesting increasing importance of iGAS in low- and middle-income settings globally.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".