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Ambulatory blood pressure monitoring in solid organ transplantation

2011· review· en· W1922741048 on OpenAlexaff
G. V. Ramesh Prasad

Bibliographic record

VenueClinical Transplantation · 2011
Typereview
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineAmbulatory blood pressureIntensive care medicineTransplantationBlood pressureAmbulatoryKidney transplantationInternal medicineKidney diseaseCardiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.830
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.139
GPT teacher head0.434
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations18
Published2011
Admission routes1
Has abstractyes

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