Alexisomia, Depression, and Adherence in Kidney Transplantation: A Preliminary Investigation
Bibliographic record
Abstract
Background: Sustained immunosuppressive adherence is essential for long-term graft survival in kidney transplant recipients. While depression and alexithymia have been linked to nonadherence, the role of alexisomia—impaired awareness of bodily signals—remains unclear. This study examined the association between alexisomia, depressive symptoms, and treatment adherence in this population. Methods: This cross-sectional study included 82 adult kidney transplant recipients with stable graft function. Treatment adherence was assessed using the Immunosuppressant Therapy Adherence Scale (ITAS). Emotional and psychosomatic traits were evaluated with the Toronto Alexithymia Scale (TAS-20), the Alexisomia Scale, and the Hospital Anxiety and Depression Scale (HADS). Patients were stratified into good vs poor adherence groups based on ITAS scores. A p-value <0.05 was considered statistically significant. Results: A total of 82 patients were included (52 with poor adherence, 30 with good adherence). The groups were similar in baseline characteristics. Alexisomia total and HADS-depression scores were significantly higher in the poor adherence group. Total TAS scores were also higher, but not statistically significant. Among alexithymia subscales, only difficulty in describing bodily emotions differed significantly. No significant differences were found in other areas of interest (Table 1). Conclusion: This preliminary study suggests that higher levels of alexisomia and depressive symptoms may be associated with nonadherence in kidney transplant recipients. Among alexithymia subdimensions, only difficulty in describing bodily emotions differed significantly, which supports the rationale to move beyond general emotional measures and consider somatic-emotional processing in adherence research. Alexisomia may be a relevant but underrecognized factor in nonadherence after kidney transplantation. Larger studies with multivariate analyses are needed to clarify its role and clinical utility.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".