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Record W2019835574 · doi:10.12968/bjca.2014.9.2.70

Identifying patients with hypertension using ABPM

2014· article· en· W2019835574 on OpenAlexaff
Noeleen Fallon, Gábrielle McKee, Caroline Finn, Rose O'Mahony, Nora Flynn, Patricia McGeary

Bibliographic record

VenueBritish Journal of Cardiac Nursing · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsTrinity College
Fundersnot available
KeywordsMedicineAmbulatory blood pressureBlood pressureAmbulatoryInternal medicineProtocol (science)PediatricsPhysical therapyAlternative medicinePathology

Abstract

fetched live from OpenAlex

The aim of this study was to assess the effectiveness of the systematic identification and sustained follow-up protocol of hypertensive patients using ambulatory blood pressure monitoring (ABPM) superimposed on Phase III cardiac rehabilitation (CR). Methods: Patients with elevated blood pressure (BP) (clinic measurement) on entry to CR had protocol cycles of ABPM, lifestyle education, medication changes and clinic BP monitoring, throughout CR and up to 6 months follow-up until BP control was achieved. Results: Initial assessment using clinic measurement identified 30% (129) patients with uncontrolled hypertension but following initial ABPM 62% (n=80) were normal with 38% (49) diagnosed as uncontrolled. These were followed-up using protocol cycles up to six times. Of those originally identified as hypertensive, 81% (104) achieved control. Conclusion: This systematic identification and sustained follow-up of hypertensive patients using ABPM in CR was a convenient, successful method in identifying and controlling a significant amount of uncontrolled hypertension.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.303
Teacher spread0.283 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2014
Admission routes1
Has abstractyes

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