Duration of corticosteroid use and long‐term outcomes after adult heart transplantation: A contemporary analysis of the International Society for Heart and Lung Transplantation Registry
Bibliographic record
Abstract
BACKGROUND: Long-term corticosteroid (CS) maintenance remains an effective option for immunosuppression following heart transplantation. We used the International Society for Heart and Lung Transplantation Registry to examine characteristics and long-term survival among heart transplant recipients with different duration of CS therapy. METHODS: Primary adult heart recipients transplanted between 2000 and 2008 who survived at least 5 years were categorized into three groups according to CS use: early withdrawal (≤2 years) (EARLY D/C), late withdrawal (between 2 and 5 years) (LATE D/C), or long-term use (>5 years) (LONG-TERM). Recipient and donor characteristics, post-transplant morbidities, and mortality were compared among groups. Kaplan-Meier was used to estimate survival up to 10 years post-transplant. RESULTS: The study cohort included 8161 recipients (2043 in EARLY D/C; 2031 in LATE D/C; and 4087 in LONG-TERM). LONG-TERM use of CS decreased over time, from 60% in 2000 to 43% in 2008, while EARLY D/C increased from 19% to 33%, respectively. Survival at 10 years after transplant was lower among the LONG-TERM group (73% vs EARLY D/C 82% vs LATE D/C 80%; P < 0.0001). CONCLUSIONS: In this large multinational cohort, the practice of long-term CS maintenance was associated with lower long-term survival compared with shorter CS use.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".