Assessment of immunosuppression induction with basiliximab compared to antithymocyte‐globulin in adult heart transplant patients
Bibliographic record
Abstract
BACKGROUND: Patients undergoing heart transplants are at risk of rejection which can have significant morbidity and mortality. Induction immunosuppression at the time of transplant reduces the early risk and has additional benefits. The induction agent of choice within our program was changed from rabbit antithymocyte-globulin (rATG) to basiliximab, so it was necessary to evaluate whether this had any impact on patient outcomes. OBJECTIVES: Our primary objective was to describe rejection, infection, and other outcomes in adult heart transplant patients at the University of Alberta Hospital in Edmonton, Canada. METHODS: This study was a nonrandomized, retrospective cohort study. RESULTS: Sixty-three patients were included with median ages 50 years versus 54 years. More female patients received rATG (20% vs. 42.4%). The most common indication for transplant in both cohorts was ICM (63.3% vs. 57.6%). Patients who received rATG had significantly higher PRA (0% vs. 43%, p < .001). Acute rejection episodes were similar between basiliximab and rATG at 3 months (16.7% vs. 15.1%; p = 1.0) and 6-months (30.0% vs. 18.1%; p = .376). Infections were not statistically different with basiliximab compared to rATG at 3-months, 43.3% vs. 63.6% and at 6-months 60.0% vs. 66.7%). There were no fatalities in either group. CONCLUSIONS: Our study did not demonstrate differences in rejection with basiliximab compared to rATG. Mortality did not differ, but basiliximab-treated patients had fewer infections and infection-related hospitalizations than those treated with rATG. Larger studies with longer durations are needed to more completely describe the differences in rejection and infectious outcomes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".