Abstract 18319: Cardiac Services for Care of Suspected Acute Coronary Syndromes in Australia and New Zealand Hospitals
Bibliographic record
Abstract
Background: Population-wide assessment of cardiac services recommended for optimal acute coronary syndromes (ACS) care has not been previously performed. We assessed the availability of ACS services among hospitals in Australia and New Zealand. Method: Services recommended for optimal ACS outcomes were identified from ACS guidelines and clinical consensus, and were collected from hospitals using a structured questionnaire. Thirty services reflecting acute care, investigations and procedures, management of complications, and preventative services were included. Data on consecutive ACS admissions over 2 weeks was simultaneously collected. Results: Of the 433 acute hospitals in both countries, 273(63%) provided service characteristics and 243(89%) provided patient data (n=2287 ACS patients). Figure shows the availability of selected services. Among hospitals, an emergency physician was available in 50%, cardiology consultation was available acutely in 35% and 22% provided primary PCI. Most investigations/procedures were available in <50% of hospitals except exercise stress testing (58%) and echocardiography (56%). A CCU was present in 52%, 44% provided an inpatient cardiology service, 52% provided a follow-up clinic and <40% provided an outpatient heart failure program or a readmissions reduction program. Service availability varied markedly among hospitals (median 14 services,IQR 7-30). Among hospitals that collected patient data, 25% of all patients presented to hospitals with <50%(15/30) of services and only 40% were cared at hospitals with >80%(24/30) services available. Service availability was not correlated with in-hospital outcomes although the study lacked sufficient power. Conclusion: The availability of cardiac services for optimal ACS care varies widely in Australian and New Zealand hospitals. Such inequalities resulting from uncoordinated dissemination of healthcare resources may contribute to variations in ACS outcomes
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".