Sustained proton pump inhibitor deprescribing among dyspeptic patients in general practice: a return to self-management through a programme of education and alginate rescue therapy. A prospective interventional study
Bibliographic record
Abstract
BACKGROUND: Dyspepsia guidelines recommend that patients treated with proton pump inhibitors (PPIs) should step down to the lowest effective dose or return to self-care, but rebound hyperacidity can make this difficult. Many patients continue on PPIs in the long term, which may lead to safety and financial implications. AIM: To determine if a nurse-led educational support programme and rescue therapy for rebound symptoms can help patients achieve a sustained reduction in PPI use. DESIGN & SETTING: A prospective interventional study was conducted at 26 surgeries across the UK. METHOD: Adult patients, treated with PPIs for ≥2 consecutive months with an active repeat prescription, were invited to a 20-minute dyspepsia clinic appointment with a trained nurse adviser. An action plan to reduce and/or stop their PPI usage was agreed and alginate supplied for the self-management of rebound symptoms. After 12 months, PPI status was reviewed and prescribing cost savings calculated. RESULTS: After 12 months, 75.1% of 6249 eligible patients stepped down or off PPIs (35.3% stepped off; 5.0% stepped down then off; 34.8% stepped down only), while 8.7% of patients had reverted to their original PPI dose. PPI prescriptions fell from 89 915 to 45 880 and alginate prescriptions increased from 2405 to 6670. An average of 1.7 bottles (500 ml each) of alginate were used per patient who stepped down or off. Estimated annual cost-saving on prescriptions was £31 716.30. CONCLUSION: A programme of education and short-term rebound symptom management helped the majority of patients to successfully step down or off PPIs, significantly reducing the potential risks associated with chronic therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".