Predicted Heart Mass: A Tale of 2 Ventricles
Bibliographic record
Abstract
BACKGROUND: Total predicted heart mass (PHM) is the recommended metric to assess donor-recipient size matching in patients undergoing heart transplantation. Separately measuring right ventricular (RV) and left ventricular (LV) PHM may improve risk prediction of 1-year graft failure. METHODS: Adult heart transplant recipients from the UNOS database from 2000 to 2018 were included in the study. LV and RV PHM were modeled as restricted cubic splines. The association with 1-year graft failure was determined using adjusted Cox regression. The risk reclassification of using both LV and RV PHM versus total PHM was assessed using the net reclassification index. RESULTS: -shaped association between total PHM and 1-year graft failure, such that risk increased for hearts undersized by >15% and those oversized by more than 27%. Graft failure incrementally increased when LV PHM was undersized by more than 5% and when RV was oversized by >20%. There was 1.5-fold greater risk of graft failure for an LV undersized by >26% or an RV oversized by more than 40%. Using LV and RV PHM risk-assessment separately led to a net reclassification index=8.5% ([95% CI, 5.3%-11.7%], nonevent net reclassification index=9.1%, event net reclassification index=-0.6%). CONCLUSIONS: The association between donor-recipient PHM match and the risk of graft failure after heart transplantation can be further understood as risk attributable to LV undersizing and RV oversizing. Assessing LV and RV PHM separately instead of total PHM could further refine the methods used to match donors and recipients for heart transplantation, minimize the risk of 1-year graft failure, and increase the use of donor organs.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".