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QUAL O MELHOR HORÁRIO DA TOMADA DA MEDICAÇÃO ANTI-HIPERTENSIVA?

2020· article· pt· W3123505520 on OpenAlexaff
Juliana Tranjan Coragem, Thiago Veiga Jardim

Bibliographic record

VenueRevista Brasileira de Hipertensão · 2020
Typearticle
Languagept
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

A hipertensão arterial é a principal causa de morte precoce no mundo.Existem diversos fatores que contribuem para sua variação dentro da fisiologia do ritmo circadiano, e que exercem forte impacto sobre o controle da pressão arterial (PA).Os valores da PA sofrem interferência de acordo com o horário da medida.Nas primeiras horas da manhã PAS aumenta rapidamente em 20 a 25 mmHg e a PAD em 10 a 15 mmHg.As drogas anti-hipertensivas, sofrem diversas influências na sua absorção, metabolização e excreção dependentes do ritmo circadiano e de suas propriedades físico-químicas.O ciclo circadiano exerce grande impacto no controle da PA, e apenas modificando o horário das prescrições, seguindo o ritmo circadiano, talvez seja possível obter melhor controle pressórico com menor dose da medicação, reduzindo assim, as possíveis reações adversas.

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)0.002
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.084
GPT teacher head0.309
Teacher spread0.225 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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