Ambulatory blood pressure monitoring in solid organ transplantation
Bibliographic record
Abstract
Solid organ transplant recipients are at an increased risk for hypertension and cardiovascular disease. To assist in their management, 24-h ambulatory blood pressure monitoring (ABPM) has become increasingly used in both clinical research settings and practice. ABPM has been used to better define post-transplant hypertension incidence and prevalence in different solid organ transplantation populations. ABPM provides additional information on cardiovascular risk beyond that obtained by clinic-based readings, based on its ability to assess 24-h blood pressure (BP) load, detect nocturnal non-dipping, and predict target organ damage. It has provided some assurance about the safety of living kidney donation. Information from ABPM can be used to guide living kidney donor selection, and because ABPM-related data has been correlated with clinically important kidney and heart transplant recipient outcomes, it may be a valuable adjunct in their management. Despite these advantages, barriers to wider use of ABPM include expense, clinical inertia in hypertension management, lack of prospective clinical trial data, and clinical problems that compete with hypertension for attention such as acute or chronic allograft dysfunction. The increasing amount of research and clinical use for ABPM may allow for closer assessment and intervention to help address the increased cardiovascular risk faced by many solid organ transplant recipients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".