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Record W4389161334 · doi:10.18066/inic0660.23

PERFIL FARMACOTERAPÊUTICO DE PACIENTES COM DIABETES MELLITUS ATENDIDOS POR FARMACÊUTICOS EM ALEGRE/ES

2023· article· pt· W4389161334 on OpenAlexaff
Larissa Couto Rosa, Eliseu Polastreli Pirovani, Bárbara Brambila Manso, Dyego Carlos Souza Anacleto de Araújo, Genival Araujo dos Santos Júnior, Kérilin Stancine Santos Rocha

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsMedicineDiabetes mellitusPhysicsComputer scienceEndocrinology

Abstract

fetched live from OpenAlex

A Diabetes Mellitus (DM) é uma doença que necessita de atenção em relação ao uso dos medicamentos e o acompanhamento farmacoterapêutico pode promover melhoria no controle dessa doença.Assim, o objetivo deste estudo foi avaliar o perfil farmacoterapêutico de pacientes com DM.Um formulário de coleta de dados foi elaborado juntamente com um manual de preenchimento.Foram atendidas 15 pessoas com DM, com média de idade foi de 64,4 anos, 80% mulheres e com média de 5,2 anos de estudo.Dentre eles, seis apresentaram glicemia aleatória acima do valor de referência e 11 apresentaram hemoglobina glicada acima do recomendado, com média geral de 9,54%.Os pacientes apresentaram consumo médio de 6,53 medicamentos, sendo a metformina o mais utilizado.As informações coletadas poderão ser utilizadas para auxiliar na realização de intervenções farmacêuticas direcionadas para cada paciente, a fim de promover o adequado manejo da DM.

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)0.002
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.315
Teacher spread0.288 · 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
Published2023
Admission routes1
Has abstractno

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