Predicted heart mass for size matching in obese heart transplant donors and recipients
Bibliographic record
Abstract
Abstract Introduction Predicted heart mass (PHM) was neither derived nor evaluated in an obese population. Our objective was to evaluate size mismatch using actual body weight or ideal body weight (IBW)‐adjusted PHM on mortality and risk assessment. Methods We conducted a retrospective cohort study of adult recipients with BMI ≥30 kg/m2 or recipients of donors with BMI≥30 kg/m2 from the ISHLT registry. We used multivariable Cox proportional hazard models to evaluate 30‐day and 1‐year mortality. The two models were compared using net reclassification index. Results 10,817 HT recipients, age 55 (IQR 46–62) years, 23% female, BMI 31 kg/m2 (IQR 28–33) were included. Donors were age 34 (IQR 24–44) years, 31% female, and BMI 31 kg/m2 (IQR 26–34). There was a significant nonlinear association between mortality and actual PHM but not IBW‐adjusted PHM. Undersizing using actual PHM was associated with higher 30‐day and 1‐year mortality (p < .01), not seen with IBW‐adjusted PHM. Actual PHM better risk classified .6% (95% CI .3–.8) patients compared to IBW‐adjusted PHM. Conclusion Actual PHM can be used for size matching when assessing mortality risk in obese recipients or recipients of obese donors. There is no advantage to re‐calculating PHM using IBW to define candidate risk at the time of organ allocation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".