Bibliographic record
Abstract
The Australian approach to dealing with acute rheumatic fever (ARF) and rheumatic heart disease (RHD) in Aboriginal and Torres Strait Islander people makes an interesting case study.Prior to the mid-1990s, there had been a few publications, largely based on hospital series or individual community experience that hinted at an enormous issue in remote Aboriginal communities of northern Australia.The year 1996 saw the publication of the first population-based data, from the Northern Territory, documenting the highest rates in the world of ARF and among the highest in the world of RHD, in the Aboriginal population. 1 Simple epidemiological analyses confirmed two obvious factors contributing to these high rates: recurrences represented more than 40% of all ARF episodes (highlighting poor delivery of benzathine penicillin G secondary prophylaxis injections); and approximately 40% of patients with RHD presented with no prior history of ARF (highlighting poor mechanisms to diagnose ARF and enter people into prevention programs in the first place).Researchers at the Menzies School of Health Research worked with colleagues in the NT Department of Health to develop a strategy based on a World Health Organization recipe for RHD control -establishment of register-based RHD control programs.A proposal was made to the Commonwealth Government, some money was cobbled together from various pots, and the Top End RHD Control Program was born in 1997.The story between then and 2009 was one of continuing research linked strongly to service delivery.The NT Control Program expanded to include Central Australia, and the two registers were combined in 2007.This program had many documented successes, particularly the ability to track existing patients, and to ensure the availability of specialist services, including cardiac surgery, to those who need it the most.However, it had trouble tackling the number one priority of control programs -improving delivery of secondary prophylaxis -in a coordinated fashion, for many reasons.Most important among these impediments was the uncertainty of funding from the Commonwealth, meaning that the program staff were not in a position to plan long-term multi-year strategies, because they weren't certain that they would be around from one year to the next.During this period, the momentum for a more national approach gathered steam, still with a strong link to research.The National Heart Foundation and the Cardiac Society of Australia and New Zealand pulled together the major researchers, clinicians and public health practitioners working in ARF/RHD in Australia and New Zealand to produce Australia's first evidence-based guidelines on this topic, published in 2006. 2 This document quickly became the source material for all approaches to ARF/RHD in Australia, and became recognised in many
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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 | 0.019 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.068 | 0.066 |
| Insufficient payload (model declined to judge) | 0.033 | 0.023 |
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".