Importance of Comprehensive Cardiovascular Screening in Patients Scheduled for Kidney Donation
Bibliographic record
Abstract
INTRODUCTION: End stage renal disease is on the rise in many parts of the world. Kidney transplant is a common procedure and definitive treatment for end stage renal disease. Along with its various advantages, it presents with an array of complications, associated with the procedure. Hence, an effective screening program to identify eligible donors is of crucial importance. The main aim of this study was to identify the frequency of possible undetected cardiovascular abnormalities in scheduled donors and its association with gender. METHODS: A sample size of 402 was selected with an equal number of donor and non-donor participants after age and gender matching. A positive electrocardiogram (ECG) change was defined as cardiac ischemia, occurring during exercise tolerance test (ETT), with 2 mm horizontal or down sloping ST-segment depression occurring 0.08 milliseconds after J-point whereas an exaggerated blood pressure (BP) was defined as high systolic blood pressure (SBP) at rest to maximum effort ≥7.5mmHg/MET (metabolic equivalents) and/or SBP at the peak of effort ≥220mmHg or subjects with high diastolic blood pressure (DBP) at rest to maximum effort ≥15mmHg, from normal levels of blood pressure at rest. Chi square was used as the primary statistical test. RESULTS: Scheduled kidney donors had significantly (P=0.007) higher proportion (n=19, 9.5%) of positive ECG changes and exaggerated BP response (n=35, 17.4%) (P<0.0001) compared with the controls. Also, female donors had significantly (P=0.025) higher (n=16, 13.2%) chances of having a positive ECG change. CONCLUSION: A significant number of kidney donors have undetected cardiovascular abnormalities which could lead to post-transplant complications. Therefore, effective screening should be made imperative to avoid preventable complications such as hypertension of kidney transplantation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".