Position paper ANMCO: Stati Generali ANMCO 2024 – La nuova organizzazione della ricerca: la proposta di ANMCO
Bibliographic record
Abstract
Since 2021, the Research Center of the Italian Association of Hospital Cardiologists (ANMCO)/Heart Care Foundation, which boasts a long tradition of research with significant impact on clinical practice, has implemented a training project to promote knowledge of the basic methodologies for clinical research and the creation of a network of young researchers currently active in numerous research studies coordinated by the same Study Center and illustrated during the ANMCO General States 2024. Among these, the EYESHOT-2 study, together with the first phase of the BRING-UP Prevention and BRING-UP3 Heart Failure studies, enrolled almost 13 000 patients with, for the last two, 97% completeness of data at the 6-month follow-up, which is expression of the high quality of observational research work. Furthermore, in collaboration with international scientific societies and research centers, the same Study Center coordinates the activities in Italy of several international multicenter studies including EuroHeart, COLT-HF, and AFFIRMO. The States General was also an opportunity to discuss the main challenges that clinical research must face in the near future, from new research methodologies, such as the use of machine learning and registry-based randomized clinical trials, to new lines of research, such as mechanistic and pathophysiological clinical studies and decentralized clinical trials for rare diseases.
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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 | 0.037 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".