The impact of the 21 November 2016 epidemic thunderstorm asthma event on general practice clinics in metropolitan Melbourne, Australia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: On 21 November 2016, parts of Victoria experienced a devastating epidemic thunderstorm asthma (ETSA) event. The aim of this study was to describe the epidemiology and burden of the 2016 ETSA event at MedicineInsight-registered general practices in the Melbourne metropolitan area in Victoria, Australia. METHOD: A cross-sectional study was conducted using patient record data from 21-23 November 2016. Codes were developed to identify all patients presenting to MedicineInsight-registered general practices with asthma during the 2016 ETSA event. RESULTS: During the event, there were 787 more asthma-related encounters to MedicineInsight general practices than expected, which represented a 7.1-fold increase (605% increase). Estimates suggest that there were between approximately 8940 and 13,689 more asthma-related encounters than expected across metropolitan Melbourne. DISCUSSION: General practices were significantly affected by the 2016 ETSA event. This work highlights the important part that general practices play in responding to ETSA events and the need for these practices to be prepared to respond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".