Respiratory diphtheria in the time of Omicron
Bibliographic record
Abstract
Abstract: . Incidence of the disease has fallen significantly since the introduction of vaccination programs; it is now rare in countries with high vaccination coverage such as Australia. This article presents the most recent respiratory cases of diphtheria in two children in New South Wales-the first locally acquired childhood cases in Australia in 30 years-and discusses potential contributing factors. These encompass the lack of clinical awareness and the delays in laboratory diagnosis in regional laboratories. The cases also highlight the problem of vaccine hesitancy and the role that primary carers play in addressing these anxieties. While clinical management of the cases progressed well, factors in the public health responses were complicated by access to appropriate care and by delays in antibiotic sensitivity profiles. The public health response to these cases raises important considerations for clinicians and public health practitioners, including preparedness for rare and re-emerging diseases, the need for culturally safe environments and the importance of addressing vaccine hesitancy. Preparedness requires consideration of the capacity of regional health systems with fewer resources and of how public health departments can support response to multiple crises. Preparedness also relies on access to necessary diagnostic laboratory resources, on up-to-date guidelines, and on maintaining awareness among clinicians for these rare infections.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".