59DE-PRESCRIBING THE PROTON PUMP INHIBITORS - STOPPING THE EPIDEMIC
Bibliographic record
Abstract
Topic: In older patients proton pump inhibitors (PPIs) are commonly inappropriately prescribed, causing potential harm (e.g. association with Clostridium Difficile infection). De-prescribing is a planned process of dose reduction or cessation of medication that is inappropriately prescribed. Our aim was to evaluate the prevalence of PPI prescription in patients admitted to our rehabilitation ward, before and after introduction of active PPI de-prescribing. Intervention: The intervention in this study was to commence active de-prescribing of PPIs on the rehabilitation ward. The guideline used was “De-prescribing proton pump inhibitors: Evidence-based clinical practice guideline” (Farrell B. Canadian Family Physician. 2017 May;63(5):354−364). Data was collected retrospectively for a 3 month period on those admitted to the ward pre-intervention, by reviewing the charts or the electronic discharge letters. Thereafter, data was actively collected for a 3 month period, after the intervention was introduced. Every patient on a PPI had the indication for the prescription reviewed, and based on guidelines was de-prescribed if appropriate (drug stopped, reduced dose, switch from regular use to “on demand”). Descriptive statistics included demographics, prevalence of an indication for PPIs and prevalence of de-prescription. Improvement: Total number of patients in study was 73, 48% (n = 35) in the pre-intervention and 52% (n = 38) in the post intervention group. Average age was 82years. In the pre-intervention group 86% (n = 30) were on PPIs upon admission, with 13% (n = 4) de-prescribed. In the post intervention group, 76% (n = 29) were on PPIs, with 52% (n = 15) de-prescribed. PPI was continued in 48% (n=14). Of these 64% (9/14) were appropriately on PPIs, with the remainder 36% (5/14) left on PPI outside guidelines. Discussion: There was a high prevalence of inappropriate PPI prescription in this cohort. The introduction of active de-prescribing resulted in a four-fold percentage increase in de-prescription. Further improvements could be achieved through involvement of a clinical pharmacist.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".