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Record W4307284539 · doi:10.1136/bmj-2021-069211

Reduce unnecessary use of proton pump inhibitors

2022· article· en· W4307284539 on OpenAlexaffabout
Barbara Farrell, Elliot Lass, Paul Moayyedi, Deanna Ward, Wade Thompson

Bibliographic record

VenueBMJ · 2022
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsUniversity of British ColumbiaMcMaster University Medical CentreThompson Rivers UniversityUniversity of OttawaBaycrest HospitalBruyèreUniversity of TorontoSinai Health SystemUniversity of Waterloo
Fundersnot available
KeywordsMedicineMedical prescriptionProton-pump inhibitorIntensive care medicineCritically illGastrointestinal bleedingPediatricsInternal medicineNursing

Abstract

fetched live from OpenAlex

### What you need to know Proton pump inhibitors (PPIs) are one of the most widely used classes of drugs globally, often taken for longer than needed and at high financial cost to society. Among adults living in the community, the point prevalence of PPI use is 7-8% in the UK and Denmark,12 while rates of use of 40-50% have been reported in older people in Canada and in those living in residential care in Australia.3456 In England, more than 50 million prescriptions for PPIs were issued in 2015.7 While PPIs are effective for upper gastrointestinal disorders and may be continued long term (beyond 8 weeks)8 for specific conditions (box 1),11121314 people often continue taking them for years when guidelines recommend 4-8 weeks of treatment or, in the case of gastrointestinal bleeding prophylaxis for critically ill patients, cessation when the patient is no longer critically ill or the risk factor triggering prophylaxis is no longer present (box 2).9131516 Box 1 ### Indications for long term use (>8 weeks) of proton pump inhibitors (PPIs)910RETURN TO TEXT

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)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.098
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0980.022

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 head0.047
GPT teacher head0.329
Teacher spread0.281 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations43
Published2022
Admission routes2
Has abstractyes

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Same venueBMJSame topicGastroesophageal reflux and treatmentsFrench-language works237,207