Bibliographic record
Abstract
Arterial hypertension (HTN) in children after kidney transplantation is an important risk factor not only for graft loss but also for cardiovascular morbidity and mortality. The prevalence of posttransplant HTN ranges between 60% and 90%. The etiology of posttransplant HTN is multifactorial and includes residual chronic native kidney disease, immunosuppressive therapy, and chronic allograft dysfunction among other causes. Clinic blood pressure (BP) should be measured at each outpatient visit. However, ambulatory blood pressure monitoring (ABPM) is the gold standard method for BP evaluation in children after kidney transplantation, as it often reveals masked and nocturnal HTN; given this, it should be regularly performed in each transplanted child. All classes of antihypertensive drugs are used in the treatment of posttransplant HTN because it has never been proven that one class is better than another. However, in several retrospective studies, the use of calcium channel blockers is associated with better graft function. The optimal target BP for transplanted children is still a matter of debate; it is recommended to target the same BP as for healthy children, that is, <95th percentile. Control of HTN in transplanted children remains poor - only 20%-50% of treated children have normal BP. There is a great potential for improvement of antihypertensive treatment that could potentially result in improvement of both graft and patient survival in children after 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".