Abstract 13746: Mental Health and Implantable Cardioverter Defibrillator Implantation in Black Patients at Risk for Sudden Cardiac Death
Bibliographic record
Abstract
Introduction: Black patients with guideline indications for implantable cardioverter defibrillators (ICD) have lower rates of implantation compared to White patients. The Educational Videos to Address Racial Disparities in ICD Therapy Via Innovative Designs (VIVID) trial enrolled self-identified Black individuals with chronic systolic heart failure and studied the impact of a video-based decision-support tool on decisional quality and ICD implantation. Hypothesis: We hypothesized that depressive symptoms are associated with greater decisional conflict and lower rates of ICD implantation among study participants. Methods: Participants were administered the Patient Health Questionnaire-2 (PHQ-2) depression screen; and the 12-Item Short-Form Health Survey, from which we derived the Mental Component Summary-12 (MCS-12). A decisional conflict scale (DCS) adapted from the Ottawa framework for shared decision-making was used to assess decisional conflict associated with the decision for ICD implantation. An analysis of covariance was used to assess differences in DCS scores. Multivariable logistic regression was used to examine the association between mental health scores and ICD implantation at 90 days. Results: Among 306 included participants, 60 (19.6%) reported depressed mood and 142 (46.4%) reported anhedonia. Participants with the lowest MCS-12 scores (poorer mental health) had greater decisional conflict regarding ICD assent compared to those with the highest MCS-12 scores (adjusted mean difference in DCS score = 3.05 [95% CI: 0.32, 5.78]). By 90-day follow-up, 204 participants (66.6%) underwent ICD implantation. There was no significant association between either PHQ-2 score or MCS-12 score and ICD implantation. Conclusions: Depressed mood and anhedonia were prevalent among Black patients considering primary prevention ICD. Poorer mental health did not impact the likelihood of ICD implantation in this 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 | 0.000 | 0.001 |
| 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) | 0.007 | 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".