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Record W3081241983 · doi:10.1111/jch.14010

Overview of blood pressure measurement by Brazilian health professionals

2020· article· en· W3081241983 on OpenAlexaff
Nila Larisse Silva de Albuquerque, Raj Padwal, Thelma Leite de Araújo

Bibliographic record

VenueJournal of Clinical Hypertension · 2020
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversity of Alberta
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMedicineProtocol (science)Blood pressureHealth professionalsNursingHealth careCuffFamily medicineSurgeryAlternative medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

Although automated monitors for blood pressure (BP) measurement are used increasingly worldwide, understanding of how such devices are used in Brazil is low. This study analyzed the status of BP measurement by Brazilian health professionals. A questionnaire regarding experience with BP measurement was sent electronically to Brazilian nurses, nursing assistants, and doctors. It had 2004 responses. Previous experience with use of automated monitors was most frequent in men (71.2%), nursing technicians (65.5%), specialists (61.1%), secondary care (71.9%), emergency care (70.6%), or the private sector (66.3%). The least complied aspects of the standardized measurement protocol were availability of various cuff sizes (53.9% and 72.9% for auscultatory and oscillometric methods, respectively) and proper calibration checks (21.5% and 46.8% for auscultatory and oscillometric methods, respectively). Brazilian health professionals report not adequately performing all the necessary aspects to measure BP in accordance with the standardized protocol in both methods, but mainly regarding the oscillometric.

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)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.008
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.299
GPT teacher head0.436
Teacher spread0.137 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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