Abstract 4128: Evaluating Sex Differences in Population-Based Utilization of Implantable Cardioverter Defibrillators (ICDs): The Role of Cardiac Conditions and Non-Cardiac Comorbidities
Bibliographic record
Abstract
BACKGROUND: Although sex differences exist in the use of ICDs, reasons for the disparities are poorly understood. We determined if age, comorbid conditions, or ICD indication explained the sex differences. METHODS: We examined all patients in Ontario, Canada, with cardiac arrest (CA, 1998 –2007), myocardial infarction (MI, 2002–2007), or heart failure (HF, 2005–2007), using the Canadian Institute for Health Information Database. MI and HF cohorts excluded those with prior CA, and included patients post-MADIT-2 and SCD-HeFT trials. Patients were followed until ICD implant using Cox regression, with hazard ratio (HR) >1.0 indicating greater likelihood of ICD implant in men. RESULTS: Among 9246 patients eligible for ICD implantation after CA, 237 (2.6%) women and 725 (7.8%) men received ICDs. In 105,516 primary prevention MI patients, 172 (0.2%) women and 836 (0.8%) men received ICDs. Among 61,160 primary prevention HF patients, 221 (0.4%) women and 852 (1.4%) men received ICDs. The rate of ICD implant was significantly higher in men across indications adjusting for age, prior arrhythmia, and comorbidities (Figure ). Post-CA, the HR for secondary prevention ICD was 1.92 (95%CI, 1.66 –2.23). Men were more likely to undergo ICD implant than women for primary prevention, with HRs 3.00 (95%CI, 2.53–3.55) post-MI and 3.01 (95%CI, 2.59 –3.50) in HF patients. Although death after primary prevention ICD did not differ by sex, mortality risk was higher in men after CA (HR 1.42; 95%CI, 1.03–1.95). CONCLUSIONS: Differences in ICD use for all indications were not explained by age or comorbidities. Despite increased use, men had reduced post-implant survival after cardiac arrest.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".