Large variations in prescriptions of gastrointestinal medications in hemodialysis patients on three continents: The Dialysis Outcomes and Practice Patterns Study (DOPPS)
Bibliographic record
Abstract
Little is known about proton pump inhibitor (PPI) or H(2) receptor antagonist (HA) prescription patterns or regarding use of predictors in hemodialysis patients. Proton pump inhibitor and HA prescribing patterns were investigated in 8628 hemodialysis patients from seven countries enrolled in the prospective, observational Dialysis Outcomes and Practice Patterns Study. Logistic regression examined predictors associated with PPI and HA use, adjusting for age, sex, country, time with end-stage renal disease, medications, 14 comorbid conditions, and the association between the number of comorbid conditions and the prescription of gastrointestinal (GI) medications. In a cross-section from February 1, 2000, 3.4% to 36.9% of patients received an HA and 0.8% to 26.9% took a PPI, depending upon the country. From 1996 to 2001, the prescription of HAs declined while PPI use increased. Facility use of HAs and PPIs ranged from 0% to 94% of patients. H2 receptor antagonist or PPI use was significantly and independently associated with age, narcotic use, corticosteroids, acetaminophen, nonsteroidal anti-inflammatory drugs, tricyclic antidepressants, selective serotonin reuptake inhibitors, coronary artery disease history, cardiovascular diseases other than hypertension or congestive heart failure, peripheral vascular disease, pulmonary disease, and GI bleed. Proton pump inhibitors or HAs were more likely to be prescribed in Italy, Spain, and the United Kingdom than in the United States. The odds of PPI prescription increased if serum phosphorus <5.5 mEq/L or serum albumin <3.5 g/dL. Prescription of GI medications was associated with many comorbidities and use of several medications. Extreme variability of prescription patterns suggests that there is no standard approach in treatment practices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".