The Impact of Health Literacy and Numeracy on Post-Transplantation Outcomes in Organ Transplant Recipients
Bibliographic record
Abstract
Background: Patients referred for solid organ transplant with limited health literacy have been shown to be less likely to have access to transplantation. We examined the association between health literacy, health numeracy and post-transplant clinical outcomes (i.e. graft failure, non-adherence, readmissions, self-efficacy, or mortality). Methods: A search of Medline for publications during the period January 1946 to July 2016 that examined health literacy, numeracy, and outcomes of transplant recipients. Titles and abstracts were independently examined by three reviewers for exclusion, and the full-text was then reviewed for inclusion. Results: Of 247 citations, 12 met inclusion criteria including one review article and five randomized control trials (RCTs). Health literacy of recipients was measured using Newest Vital Sign (NVS) (n=2), Short Test of Functional Health Literacy in Adults (STOHFLA) (n=2), Rapid Estimate of Adult Literacy in Medicine (REALM-T) (n=1), and other knowledge questionnaires (n=5). Level of formal education was also examined as an assay of health literacy (n=3). Post-transplant outcomes were assessed through medication adherence (n=4), skin cancer incidence (n=2), graft loss (n=1), recipient mortality (n=1), kidney function (n=1), health-related quality of life (n=1), and self-efficacy (n=1). Eleven citations found limited health literacy to be associated with adverse post-transplant clinical outcomes, and one citation found no association between health literacy and non-adherence. Health numeracy was not studied in any of the citations. Conclusion: Health literacy is negatively associated with adverse post-transplant clinical outcomes. Future studies should analyze the association between health numeracy and clinical outcomes after transplant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".