Development and Validation of the Kidney Transplant Understanding Tool (K-TUT)
Bibliographic record
Abstract
Background Several educational interventions have been designed to improve patient knowledge before and after kidney transplantation. However, evaluation of such interventions has been difficult because validated instruments to measure knowledge-based outcomes in this population have not been developed. Objective To create a tool to measure patient knowledge of kidney transplantation and to evaluate its validity. Methods The Kidney Transplant Understanding Tool (K-TUT) was created using a stepwise iterative process. Experts in the field and transplant recipients were consulted to establish content validity. The K-TUT consists of 9 true/false and 13 multiple-choice questions, and scores are based on the number correct answers [YES/NO format] of 69 items. The questionnaire was piloted in a study that also measured health literacy (via the Short Test of Functional Health Literacy) in transplant candidates, whereas the main survey was mailed to transplant recipients. Test-retest was performed, and completed surveys were analyzed for internal consistency, construct validity, floor and ceiling effects, and reproducibility. Results Surveys were offered to 106 pretransplant patients and 235 in the posttransplant period, and response rates were 38.7% (41/106) and 63.4% (149/235), respectively. The mean corrected scores were 53.1 ± 8.5 (77%) and 56.2 ± 6.3 (81%), respectively. Test-retest was performed over 20% of both cohorts and percent agreement ranged between 70% and 100% in the pretransplant group and 66% and 100% in the posttransplant group. Cronbach α ranged from 0.794 to 0.875 in all cohorts indicating favorable internal consistency. Increased health literacy in the pretransplant group was significantly associated with increased knowledge (r = 0.52; P < 0.001), suggestive of construct validity, and the absence of floor and ceiling effects was positive. The majority of transplant recipients (98/148, 67%) believed the questionnaire adequately assessed transplant knowledge, about a quarter (36/148, 24.3%) were “unsure,” and 85% (126/148) agreed that no questions should be removed. Conclusions Although more study is warranted to further assess psychometric properties, the K-TUT appears to be a promising tool to measure transplant knowledge.
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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 | 0.041 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".