Depression Diagnoses After Living Kidney Donation
Bibliographic record
Abstract
BACKGROUND: Limited data exist on correlates of psychological outcomes after kidney donation. METHODS: We used a database integrating Organ Procurement and Transplantation Network registrations for 4650 living kidney donors from 1987 to 2007 with administrative data of a U.S. private health insurer (2000-2007 claims) to identify depression diagnoses among prior living donors. The burden and demographic correlates of depression after enrollment in the insurance plan were estimated by Cox regression. Graft failure and death of the donor's recipient were examined as time-varying exposures. RESULTS: After start of insurance benefits, the cumulative frequency of depression diagnosis was 4.2% at 1 year and 11.5% at 5 years, and depression among donors was less common than among age- and gender-matched general insurance beneficiaries (rate ratio, 0.70; 95% confidence intervals [CI], 0.60-0.81). Demographic and clinical correlates of increased likelihood of depression diagnoses among the prior donors included female gender, white race, and some perioperative complications. After adjustment for donor demographic factors, recipient death (adjusted hazard ratio (aHR), 2.23; 95% CI, 1.11-4.48) and death-censored graft failure (aHR, 3.30; 95% CI, 1.49-7.34) were associated with two to three times the relative risk of subsequent depression diagnosis among nonspousal unrelated donors. There were trends toward increased depression diagnoses after recipient death and graft failure among spousal donors but no evidence of associations of these recipient events with the likelihood of depression diagnosis among related donors. CONCLUSIONS: Recipient death and graft loss predict increased depression risk among unrelated living donors in this privately insured sample. Informed consent and postdonation care should consider the potential impact of recipient outcomes on the psychological health of the donor.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".