Population-Level Sex Differences and Predictors for Treatment With Catheter Ablation in Patients With Atrial Fibrillation and Heart Failure
Bibliographic record
Abstract
Background: Current guidelines are relatively general regarding the type of patient with heart failure (HF) who should be considered for catheter ablation (CA) of atrial fibrillation (AF). The aim of the present study was to identify clinical predictors and sex differences for treatment with CA in the AF-HF population. Methods: A population-based AF-HF cohort was created using the Quebec administrative data (2000-2017). Patients were followed from the date of diagnosis of both diseases to the date of CA or death. Predictors for CA, represented by time-varying covariates, were assessed in a multivariable Cox model that accounted for the competing risk of death. Results: Among 101,931 patients with AF-HF with medication information (median age, 80.7 years; interquartile range [IQR], 73.9-86.3; 51.4% were female, median CHA 2 DS 2 -VASc, 4; IQR, 3-4), only 432 R ESUM E Contexte : Les lignes directrices actuelles abordent de faon relativement g en erale les cas d'insuffisance cardiaque (IC) o les patients devraient tre consid er es comme des candidats l'ablation par cath eter (AC) pour le traitement de la fibrillation auriculaire (FA). La pr esente etude visait cerner les pr edicteurs cliniques et les diff erences entre les sexes dans le contexte de l'AC au sein de la population atteinte de FA et d'IC. M ethodologie : Une cohorte populationnelle de patients atteints de FA et d'IC a et e constitu ee partir de donn ees administratives du Qu ebec (2000-2017). Le suivi des patients allait de la date du diagnostic des deux affections la date de l'AC ou du d ecs. Les pr edicteurs d'AC, repr esent es par des covariables temporalis ees, ont et e evalu es dans un modle de Cox multivari e tenant compte du risque concurrent de d ecs.
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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) | 0.000 | 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".