Red blood cell distribution width to predict mortality in heart transplant recipients: a systematic review
Bibliographic record
Abstract
Red blood cell distribution width (RDW) has been shown to have prognostic value in a number of different clinical settings, such as cardiovascular disease, including heart failure. However, its prognostic value in heart transplant (HT) recipients remains unknown. The aim of this systematic review is to determine the prognostic value of pre-transplant RDW for mortality in HT recipients. There is a pre-published protocol of this review. The terms "Heart transplant", "Red cell distribution width" and their synonyms were used in the search strategy. PubMed/Medline, Embase, Scopus, Web of Science and LILACS were searched until May 17th, 2022, without date or language restrictions. Two authors independently carried out the selection, first by title and abstract, second by full-text revision. Discrepancies were discussed and resolved with three other authors. Quality of individual studies was assessed with Newcastle Ottawa Scale (NOS) for cohorts. After removing the duplicates, 3885 articles were identified. Four articles were included in the qualitative synthesis. Three studies were classified as "good quality": whereas one as "poor quality" according to NOS scale. All the included articles evaluated long-term mortality and one study also evaluated short-term mortality. In this one, a correlation between higher RDW values and short-term mortality was reported. Meanwhile, in all the studies, a high pre-HT RDW was a marker of long-term mortality following cardiac transplantation. Our review shows that an elevated on-admission RDW is associated with long-term mortality in heart transplantation recipients.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".