Abstract 9988: Racial/Ethnic Disparities in the Patients Undergoing Heart Transplantation: Insights from National Inpatient Sample 2016-2018
Bibliographic record
Abstract
Background: There is a paucity of data on in-hospital outcomes of Heart Transplant (HT) recipients among different races. This study aimed to evaluate the impact of race on the clinical outcomes in the patients undergoing HT. Methods: We conducted a retrospective analysis of adult hospitalizations from the National Inpatient Sample (NIS) between 2016 and 2018. ICD codes were used to identify those who underwent HT. Prevalence estimates were weighted using NIS-provided discharge-level weights to reflect national estimates. Weighted multivariable logistic regression was used to assess the association of race and various clinical outcomes in those who underwent HT. Caucasians were kept as the reference category. These models were adjusted for several patient-level and hospital-level characteristics. Results: Among 7945 HT hospitalizations, 4835 (43.2%) were Caucasian, 1730 (41.7%) were African American, 814 (10.1%) were Hispanics and 565 (10.1%) were Others. After adjustment of variables, there was no significant difference in all-cause mortality, cardiac arrest, invasive mechanical ventilation, use of pressors, tracheostomy, gastrostomy, vascular complications, bleeding requiring transfusion, stroke or acute kidney injury in African-Americans, Hispanics and others when compared to Caucasians (Table 2). Conclusions: We did not find a significant difference in various hospital outcomes of HT recipients among different races.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".