Health Insurance Status of US Living Kidney Donors
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Ensuring follow-up of living kidney donors (LKDs) is essential to long-term preventive care. We sought information on health insurance status of US LKDs, with particular attention to age, gender, and ethnicity. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The United Network for Organ Sharing/Organ Procurement Transplantation Network database was queried for associations among age at donation, race, gender, and health insurance status. We studied all US LKDs between July 2004 and September 2006. RESULTS: A total of 10,021 LKDs with known health insurance status were studied, 1765 (18%) of whom lacked health insurance at donation. There were 4852 donors without health insurance information. Younger kidney donors had higher rates of being uninsured (age 18 to 34: 26.2%; age 35 to 49: 15.2%; age 50 to 64: 11.2%; age >65: 3.8%; P < 0.0001), as did men (19.5 versus 16.3% for women; P < 0.0001), and ethnic minorities (white 13.4%, black 21%, Hispanic 35.6%, Asian 26.7%; P < 0.0001). CONCLUSIONS: This study confirms that younger patients, ethnic minorities, and men are less likely to have health insurance when donating a kidney, which could negatively affect adherence to long-term follow-up.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".