Seventh TRM Forum on Computer Simulation and Experimental Assessment of Cardiac Function: Creating the Basis for Tailored Therapies
Bibliographic record
Abstract
Computer simulations have gained increasing importance for the understanding of cardiac arrhythmias and the development of new therapeutic solutions, overcoming some of the limitations encountered in clinical and experimental research. Progresses in computer models depend on continuous exchange of clinical and experimental data, both for model development and validation. Therefore, it is important that experts from these two fields communicate. Today, patient-specific models based on clinical imaging and electrophysiological data are becoming a reality. Such models could be used in the future for guiding individual clinical antiarrhythmic therapies. The challenge lies in how to translate these computer methods into clinically effective tools. To that end, since 1998 the Theo Rossi di Montelera (TRM) forum has brought together researchers with different expertise in computer modelling, experimental and clinical research. The seventh TRM forum was held in Lugano, Switzerland, on 1–3 December 2013, hosted by the Lausanne Heart group, the University of Lugano (USI) and the Cardiocentro Ticino (CCT). The theme was ‘Creating the Basis for Tailored Therapies’. The objective of the TRM forum is to facilitate the translation of basic science findings in computer modelling into the clinical field. The first day of the forum was therefore focused on ‘From computer to bedside: how predictive are computer models’ with the presentation of latest patient-specific computer models of the atria and ventricles and how they can be used to predict the effect of cardiac therapies. The second day followed the opposite pathway ‘From bedside …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".