Pediatric heart transplantation across a positive crossmatch: First year results from the CTOTC-04 multi-institutional study
Bibliographic record
Abstract
Sensitization is common in pediatric heart transplant candidates and waitlist mortality is high. Transplantation across a positive crossmatch may reduce wait time, but is considered high risk. We prospectively recruited consecutive candidates at eight North American centers. At transplantation, subjects were categorized as nonsensitized or sensitized (presence of ≥1 HLA antibody with MFI ≥1000 using single antigen beads). Sensitized subjects were further classified as complement-dependent cytotoxicity crossmatch (CDC-crossmatch) positive or negative and as donor-specific antibodies (DSA) positive or negative. Immunosuppression was standardized. CDC-crossmatch-positive subjects also received perioperative antibody removal, maintenance corticosteroids, and intravenous immunoglobulin. The primary endpoint was the 1 year incidence rate of a composite of death, retransplantation, or rejection with hemodynamic compromise. 317 subjects were screened, 290 enrolled and 240 transplanted (51 with pretransplant DSA, 11 with positive CDC-crossmatch). The incidence rates of the primary endpoint did not differ statistically between groups; nonsensitized 6.7% (CI: 2.7%, 13.3%), sensitized crossmatch positive 18.2% (CI: 2.3%, 51.8%), sensitized crossmatch negative 10.7% (CI: 5.7%, 18.0%), P = .2354. The primary endpoint also did not differ by DSA status. Freedom from antibody-mediated and cellular rejection was lower in the crossmatch positive group and/or in the presence of DSA. Follow-up will determine if acceptable outcomes can be achieved long-term.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".