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Record W2589011509 · doi:10.1097/mnh.0000000000000316

Intensive blood pressure lowering in chronic kidney disease

2017· review· en· W2589011509 on OpenAlexaffabout
Marcel Ruzicka, Swapnil Hiremath

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2017
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversity of OttawaOttawa Hospital
Fundersnot available
KeywordsMedicineKidney diseaseBlood pressureIntensive care medicineStroke (engine)Diabetes mellitusHeart failurePopulationDiseaseClinical trialCardiologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Release of the findings from the Systolic Blood Pressure Intervention Trial has resulted in a renewed examination of intensive blood pressure (BP) lowering. Only a few national hypertension guidelines (Canada and Australia) have changed recommendations, but considerable heterogeneity still exists with respect to the patient population to whom intensive BP lowering may apply. RECENT FINDINGS: There is fairly robust evidence that lower BP targets in nondiabetic chronic kidney disease (CKD) results in a decrease in heart failure and mortality. Similar data exist in patients with diabetes and CKD for reduction in stroke. Consideration of the differences in BP measurement methods in newer trials helps us understand and interpret the findings. SUMMARY: Though often times less is more with respect to therapeutic measures, in patients with CKD, more BP lowering will result in more cardiovascular benefit. Use of newer oscillometric BP devices with adequate resting prior and judicious patient selection are the key aspects to be considered when applying intensive BP lowering.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.153
GPT teacher head0.393
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2017
Admission routes2
Has abstractyes

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