Disparities in Referral and Utilization of Implantable Cardioverter-Defibrillators for Primary Prevention of Sudden Cardiac Death
Bibliographic record
Abstract
BACKGROUND: Implantable cardioverter-defibrillators (ICDs) reduce mortality in patients with reduced left ventricular ejection fraction (LVEF). We investigated sex disparities in a contemporary Canadian population for utilization of primary prevention ICDs. METHODS: This was a retrospective cohort study on patients with reduced LVEF admitted to hospitals from 2010 to 2020 in Nova Scotia (population = 971,935). RESULTS: There were 4406 patients eligible for ICDs: 3108 (71%) men and 1298 (29%) women. The mean follow-up time was 3.9 ± 3.0 years. Rates of coronary disease were similar between men and women (45.8% vs 44.0%; P = 0.28), but men had lower LVEF (26.6 ± 5.9% vs 27.2 ± 5.8%; P = 0.0017). The referral rate for ICD was 11% (n = 487), with 13% of men (n = 403) and 6.5% of women (n = 84) referred (P < 0.001). The ICD implantation rate in the population was 8% (n = 358), with 9.5% of men (n = 296) and 4.8% of women (n = 62) (P < 0.001) receiving the device. Men were more likely than women to receive an ICD (odds ratio 2.08, 95% confidence interval 1.61-2.70; P < 0.0001)). There was no significant difference in mortality between men and women (P = 0.2764). There was no significant difference in device therapies between men and women (43.8% vs 31.1%; P = 0.0685). CONCLUSIONS: A significant disparity exists in the utilization of primary prevention ICDs between men and women in a contemporary Canadian population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".