Pedro Brugada and Peter Schwartz share the Lefoulon-Delalande Foundation Scientific Prize 2019
Bibliographic record
Abstract
Pedro Brugada and Peter Schwartz with the prestigious Grand Prix Scientifique 2019 award of e600 000 for their research on the Brugada syndrome and long QT syndrome (LQTS) respectively. Pedro BrugadaFrom 1986 when Pedro Brugada saw the first patient, until today, the LQTS (Brugada) has been his main area of research.Brugada syndrome promoted the entry of genetics into cardiovascular medicine when it became evident that it is a hereditary condition.This disease has also had an impact at other levels e.g.better diagnosis and treatment of children with syncope where the causes were previously not understood.The condition has also promoted the development of special technologies as pacemakers and defibrillators, to be used in children and young adults.The disease has also made it possible to understand phenomena such as, sudden death caused by medications used by individuals that did not know that they suffered from the disease.Research into this disease has shown that there are a number of genetic types that behave differently.Some individuals with the disease may die if they develop fever, while others not.Many patients with this disease die during rest, but some special forms may lead to sudden death during exercise.The present research of Pedro Brugada is related to Brugada syndrome with a focus for a better and early recognition of the disease, and a search for genetic tools that may allow us to someday cure the disease by correcting the genetic defect.This prize is a very welcome support to continue this research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.078 | 0.055 |
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".