Response to “Postoperative Cognitive Dysfunction in Heart Transplantation Recipients”
Bibliographic record
Abstract
Dear Editor-in-Chief, I am writing to provide feedback on the recently published article entitled Postoperative Cognitive Dysfunction in Heart Transplantation Recipients (Issue 38: e15337, 2024) [1] in Clinical Transplantation. The article by Qin et al. offers a compelling analysis of postoperative neurocognitive disorders (NCD) in heart transplant recipients, reporting a high incidence (63.2%) of cognitive dysfunction in a study sample of 76 participants. While the findings are significant, I would like to raise some issues. First, the authors defined postoperative neurocognitive disorder (NCD) as cognitive dysfunction occurring within 12 months after surgery [2]. However, the study sample had a mean transplantation interval of 5.42 ± 2.76 years (5.20 ± 2.41 years in the NCD group). This raises questions about whether the observed cognitive deficits can still be classified as NCD, given the extended duration following the original heart transplant procedure. Second, the study exclusively used two global cognitive screening tools, the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), to evaluate cognitive outcomes. While these measures are practical in clinical settings and widely used in individuals with heart failure, [3] the leading indication for heart transplantation in the study, they lack specificity in multidimensional cognitive evaluation. Comprehensive neuropsychological test batteries would provide a more nuanced assessment, identifying specific deficits, determining severity, and evaluating functional limitations [4]. Furthermore, relying on single-domain impairment as a criterion and using traditional cut-off scores (< 26 for MoCA and < 24 for MMSE) may underestimate the incidence of cognitive deficits in the study sample [3]. Addressing these methodological considerations could significantly enhance our understanding of cognitive impairment in heart transplant recipients and contribute to refining the recovery trajectory within the field of transplantation. Sincerely, Tao Zheng, MN, RN, CCRN-CSC-CMC, CHFN, PCCN PhD Candidate/Pre-doctoral Fellow The development this manuscript was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number F31NR019924 and a Sigma Theta Tau Critical Care Grant from the American Association of Critical Care Nurses. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health. The authors declare no conflicts of interest.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.025 | 0.023 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".