The Positive Association between Proton Pump Inhibitors and Clostridium Difficile Infection
Bibliographic record
Abstract
BACKGROUND: Proton pump inhibitors (PPIs) are among the most commonly used medications by patients due to its availability over the counter and frequent prescribing by physicians to treat and alleviate symptoms of gastroesophageal reflux disease. Recently, the FDA issued a warning with respect to the utilization of PPIs and risk of developing Clostridium difficile infections (CDI). The most commonly known medications to cause CDI are antibiotics. However, available studies suggest an association and increase in risk for CDI with PPI use as well. OBJECTIVE: The purpose of this research is to review and summarize data currently available on the association between PPIs and CDI. METHODS: To search for eligible studies, EBSCO engines were investigated using proton pump inhibitors or PPIs and Clostridium difficile or C. diff. as search terms. Meta analyses and systematic reviews published between 2000 and 2020 on adult patients were considered. RESULTS: Eight meta-analyses and systematic reviews met the inclusion criteria. They included studies conducted in the US, Europe, Asia and Canada on inpatient and outpatient adults. The final result for all 8 studies showed a statistically significant association between PPIs and CDI ranging from mild to high risk. CONCLUSION: Currently available data suggest a positive association between PPIs and CDI.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".