Síndrome de Bayés, accidente cerebrovascular y demencia
Bibliographic record
Abstract
Bayés's síndrome is a clinical entity based on the association between advanced interatrial block and the development of supraventricular tachyarrhythmia, being atrial fibrillation (AF), most frequent. This association was discovered by Prof. Antoni Bayés de Luna in the'80s. Further studies by other groups found a strong relationship between Bayés's syndrome and thromboembolic phenomena, being stroke the most serious. Moreover, patients with this syndrome has an increased incidence of cognitive impairment and dementia. This observation triggered the question about whether the use of an anticoagulation therapy prior to the documentation of AF could prevent A-IAB associated thromboembolic events. There are ongoing studies in different phases of development aiming to compare the efficacy of anticoagulation in patients with A-IAB with no prior documentation of AF. The outcomes of these studies will allow determining the efficacy of this early therapeutic intervention, and help deciding the role of anticoagulation in patients with A-IAB and no demonstrated AF.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".