Economic and ethical impact of extrarenal findings on potential living kidney donor assessment with computed tomography angiography
Bibliographic record
Abstract
To determine the prevalence and spectrum of extrarenal findings in a screening population of potential living kidney donors undergoing renal Computed tomography angiography (CTA) and evaluate their impact on subsequent patient management and imaging costs. Two radiologists retrospectively reviewed 175 consecutive renal CTA's performed for assessment of potential living kidney donors. Extrarenal radiological findings were recorded and classified according to high, medium, or low importance based on clinical relevance and the need for further investigations and/or treatment. The cost of additional imaging examinations was calculated using 2002 Canadian (British Columbia) reimbursements. There were 73 extrarenal findings in 71/175 (40.6%) of the potential kidney donors in the study population. Findings were categorized as of high clinical importance in 18 (10.3%) cases, including lung lesions, bowel tumors, and liver tumors and as medium importance in 31 (17.7%). Twenty-two (12.6%) individuals had findings categorized as low importance, probably of no clinical significance and requiring no follow-up. Further potential evaluation of the 49 patients (28%) with highly and moderately significant extrarenal findings may require an additional $6137 (mean $35.1 per each case of all the screened patients). Transplantation of a kidney from a living donor is an excellent alternative to cadaveric allografts. Potential living kidney donors are a highly selected population of healthy individuals, screened for significant past or current medical conditions before undergoing CTA. Despite this screening, potentially significant extrarenal findings (classified as high or medium importance) were revealed in 28% of patients. These patients may require further investigations and/or treatment. The referring physician and patient should be aware of such potentially high probability, which may require further nontransplant related evaluation and treatment. This has medical, legal, economic, and ethical implications.
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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".