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Record W2151137502 · doi:10.1136/ebn.8.4.112

Review: self monitoring interventions modestly reduce diastolic blood pressure (BP) but do not improve BP control in hypertension

2005· letter· en· W2151137502 on OpenAlexaff
Ruth Martin‐Misener

Bibliographic record

VenueEvidence-Based Nursing · 2005
Typeletter
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicinePsychological interventionCochrane LibraryBlood pressureMEDLINEPharmacistRandomized controlled trialProtocol (science)Physical therapyEmergency medicineFamily medicineNursingInternal medicineAlternative medicinePharmacy

Abstract

fetched live from OpenAlex

Fahey T, Schroeder K, Ebrahim S. Interventions used to improve control of blood pressure in patients with hypertension. Cochrane Database Syst Rev 2005;(1):CD005182. Q How effective are various models of care for improving blood pressure (BP) control in patients with hypertension? ### ![Graphic][1] Data sources: Medline and EMBASE/Excerpta Medica (2000 to November 2002), Cochrane Library (Issue 3, 2002); hand searches of references of retrieved articles; and experts. ### ![Graphic][2] Study selection and assessment: randomised controlled trials (RCTs) in any language that included patients ⩾18 years of age with primary hypertension (treated or not currently treated with BP lowering drugs) in a primary care, outpatient, or community setting; and compared self monitoring, patient education, physician education, health professional (nurse or pharmacist) led care, protocol driven care (organisational interventions to improve delivery of care), or appointment reminders with no intervention or usual care. Study quality was assessed … [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.gif

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)0.004
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0180.003

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 head0.084
GPT teacher head0.337
Teacher spread0.253 · 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 designSystematic review
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

Citations0
Published2005
Admission routes1
Has abstractyes

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