Cardiovascular Events and Investigation in Patients Who Are Awaiting Cadaveric Kidney Transplantation
Bibliographic record
Abstract
The optimal strategy for cardiovascular (CV) disease surveillance in kidney transplant candidates is uncertain. In this observational study of 604 wait-listed patients in British Columbia, the risk for CV event in diabetic and nondiabetic candidates was 12.7 and 4.5% per year, respectively. CV event rates were relatively constant during the first 3 yr of wait-listing (5.3 to 6.6 per 100 patient-years; 95% confidence interval [CI], 3.7 to 9.3) but rose dramatically during the peritransplantation period (39.6/100 patient-years; 95% CI, 20.6 to 76.1) and remained high throughout the first posttransplantation year (4.0 per 100 patient-years; 95% CI, 2.2 to 7.5). The results of noninvasive cardiac investigations before wait-listing were not predictive of the time to CV event after wait-listing. The practice of surveillance cardiac investigation in wait-listed patients on the basis of ongoing clinical assessment of cardiac risk resulted in fewer investigations (n = 171) than with the recommended practice of periodic screening on the basis of waiting time alone (n = 530) and was not associated with an increased frequency of CV events (CV event rate in patients with and without the recommended frequency of investigation was 9.9 [95% CI, 7.1 to 13.7] and 6.7 [95% CI, 5.2 to 8.7] per 100 patient-years). It is concluded that transplant candidates are at high risk for CV events particularly during the perioperative period. Initial cardiac investigations have limited value in guiding the timing of patient reevaluation after wait-listing. Periodic surveillance cardiac investigation after wait-listing may be unnecessary and requires further study.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".