Psychosocial determinants of healthcare use costs in kidney transplant recipients
Bibliographic record
Abstract
Introduction: Psychosocial factors frequently occur in kidney transplant recipients (KTRs), leading to behavioral alterations and reduced therapeutic adherence. However, the burden of psychosocial disorders on costs for KTRs is unknown. The aim of the study is to identify predictors of healthcare costs due to hospital admissions and emergency department access in KTRs. Methods: This is a longitudinal observational study conducted on KTRs aged >18 years, excluding patients with an insufficient level of autonomy and cognitive disorder. KTRs underwent psychosocial assessment via two interviews, namely the Mini-International Neuropsychiatric Interview 6.0 (MINI 6.0) and the Diagnostic Criteria for Psychosomatic Research Interview (DCPR) and via the Edmonton Symptom Assessment System Revised (ESAS-R) scale, a self-administrated questionnaire. Sociodemographic data and healthcare costs for hospital admissions and emergency department access were collected in the 2016-2021 period. Psychosocial determinants were as follows: (1) ESAS-R psychological and physical score; (2) symptomatic clusters determined by DCPR (illness behavior cluster, somatization cluster, and personological cluster); and (3) ICD diagnosis of adjustment disorder, anxiety disorder, and mood disorder. A multivariate regression model was used to test the association between psychosocial determinants and total healthcare costs. Results: < 0.001) were positively associated with costs due to total healthcare costs. Conclusions: This study showed somatization and mood disorders could predict costs for hospital admissions and emergency department access and be possible risk factors for poor outcomes, including death, in KTRs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".