Position paper ANMCO: Stati Generali 2023 – La medicina digitale in cardiologia: evidenze e stato di avanzamento in Italia
Bibliographic record
Abstract
Technological innovation provides easily accessible tools capable of simplifying healthcare processes. Notably, digital technology application in the cardiology field can improve prognosis, reduce costs, and lead to an overall improvement in healthcare. The digitization of health data, with the use of electronic health records and of electronic health files in Italy, represents one of the fields of application of digital technologies in medicine. The 2023 States General of the Italian Association of Hospital Cardiologists (ANMCO) provided an opportunity to focus attention on the potential benefits and critical issues associated with the implementation of the aforementioned digital tools, artificial intelligence, and telecardiology. This document summarizes key aspects that emerged during the event.
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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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".