Performance of acute coronary syndrome approaches in Brazil: a report from the BRACE (Brazilian Registry in Acute Coronary SyndromEs)
Bibliographic record
Abstract
AIMS: Diagnostic and therapeutic tools have a significant impact on morbidity and mortality associated with acute coronary syndromes (ACS). Data about ACS performance measures are scarce in Brazil, and improving its collection is an objective of the Brazilian Registry in Acute Coronary syndromEs (BRACE). METHODS AND RESULTS: The BRACE is a cross-sectional, observational epidemiological registry of ACS patients. Stratified 'cluster sampling' methodology was adopted to obtain a representative picture of ACS. A performance score (PS) varying from 0 to 100 was developed to compare studied parameters. Performance measures alone and the PS were compared between institutions, and the relationship between the PS and outcomes was evaluated. A total of 1150 patients, median age 63 years, 64% male, from 72 hospitals were included in the registry. The mean PS for the overall population was 65.9% ± 20.1%. Teaching institutions had a significantly higher PS (71.4% ± 16.9%) compared with non-teaching hospitals (63.4% ± 21%; P < 0.001). Overall in-hospital mortality was 5.2%, and the variables that correlated independently with in-hospital mortality included: PS-per point increase (OR = 0.97, 95% CI 0.95-0.98, P < 0.001), age-per year (OR = 1.06, 95% CI 1.03-1.09, P < 0.001), chronic kidney disease (OR = 3.12, 95% CI 1.08-9.00, P = 0.036), and prior angioplasty (OR = 0.25, 95% CI 0.07-0.84, P = 0.025). CONCLUSIONS: In BRACE, the adoption of evidence-based therapies for ACS, as measured by the performance score, was independently associated with lower in-hospital mortality. The use of diagnostic tools and therapeutic approaches for the management of ACS is less than ideal in Brazil, with high variability especially among different regions of the country.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".