Non-steroidal anti-inflammatory drugs in chronic kidney disease: a systematic review of prescription practices and use in primary care
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) management focuses on limiting further renal injury, including avoiding nephrotoxic medications such as non-steroidal anti-inflammatory drugs (NSAIDs). We performed a systematic review to evaluate the prevalence of primary care NSAID prescribing in this population. METHODS: We systematically searched MEDLINE and Embase from inception to October 2017 for observational studies examining NSAID prescribing practices or use in CKD patients in a primary care setting. The methodological quality of included studies was assessed independently by two authors using a modified version of the Agency for Healthcare Research and Quality's Methodological Evaluation of Observational Research checklist. RESULTS: Our search generated 8055 potentially relevant publications, 304 of which were retrieved for full-text review. A total of 14 studies from 13 publications met our inclusion criteria. There were eight cohort and three cross-sectional studies, two quality improvement intervention studies and one prospective survey, representing a total of 49 209 CKD patients. Cross-sectional point prevalence of NSAID use in CKD patients ranged from 8 to 21%. Annual period prevalence rates ranged from 3 to 33%. Meta-analysis was not performed due to important clinical heterogeneity across study populations. CONCLUSIONS: Evidence suggests that NSAID prescriptions/use in primary care among patients with CKD is variable and relatively high. Future research should explore reasons for this to better focus knowledge translation interventions aimed at reducing NSAID use in this patient population.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".