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Record W4406609575 · doi:10.1590/s2175-97902025e24168

Proton pump inhibitor leaflets: Is there information on deprescribing?

2025· article· en· W4406609575 on OpenAlexaboutno aff
Marcus Vinícius Lopes Campos, Farah Maria Drumond Chequer, Luanna Gabriella Resende da Silva, André Oliveira Baldoni

Bibliographic record

VenueBrazilian Journal of Pharmaceutical Sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsDeprescribingProton-pump inhibitorProtonMedicinePharmacologyChemistryInternal medicinePolypharmacyPhysics

Abstract

fetched live from OpenAlex

Abstract This study aims to analyze the existence of information about deprescription in the leaflets of proton pump inhibitor (PPI) medications. The leaflets available on the Brazilian Health Regulatory Agency (ANVISA) and Food and Drug Administration (FDA) websites for the following medications were analyzed: omeprazole, esomeprazole, lansoprazole, dexlansoprazole, pantoprazole, and rabeprazole. The variables collected in each leaflet were the existence: a) about deprescribing; b) of guidance on the deprescription process; c) maximum recommended time for use; and d) risk of prolonged use. This information was analyzed in accordance with the PPI deprescription guideline, from Canada. Regarding the medication leaflets, 83.33 % from ANVISA and 100 % from the FDA did not present explicit and systematic guidance on deprescribing. Regarding the maximum time of use, 100 % of the leaflets from both agencies contained this information. Regarding the risks of prolonged use of the medication, 33.33 % of the ANVISA leaflets and 33.3 % of the FDA leaflets did not report the increased risks described in the guideline. The results highlight a large gap in information about deprescribing in PPI leaflets; this highlighting is necessary to contribute to the promotion of the rational use of medicines.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

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

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 head0.034
GPT teacher head0.373
Teacher spread0.339 · 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 teacher head, 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
Published2025
Admission routes1
Has abstractyes

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