EuroHeart and the National Outcomes Evaluation Programme in Italy: relevance and perspectives
Bibliographic record
Abstract
Equitable care implies that all patients are given an equal possibility to receive the same (and ideally the best attainable) care. Therefore, the inclusion of the entire population of interest—i.e. consecutive sampling—is a requisite for equity assessment. This is relevant in general for observational studies but critical when we are aiming at quality improvement, especially for deadly conditions with documented evidence-to-practice gap such as acute coronary syndrome (ACS) to mitigate the risk of selection bias. Consecutive inclusion, however, is often mentioned but rarely verified and quantified.1 Administrative data required by national health authorities provide an opportunity to verify consecutive inclusion.2 In Italy the National Outcomes Evaluation Programme (PNE) developed by Italian National Agency for Regional Healthcare Services (AgeNaS) on behalf of the Health Ministry exemplifies this opportunity.3 Within this context, developing projects aimed at enhancing health data collections through integration with administrative data represents an ideal perspective. In this regard, the European Unified Registries for Heart Care Evaluation and Randomized Trials (EuroHeart) is an international quality improvement collaboration initiated and supported by the European Society of Cardiology (ESC) that aims to improve the quality of cardiovascular care through continuous capture of individual patient data in several ESC-affiliated countries, including Italy.4
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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 | 0.263 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".