Sick Day Medication Guidance for People With Diabetes, Kidney Disease, or Cardiovascular Disease: A Systematic Scoping Review
Bibliographic record
Abstract
Rationale & Objective: Sick day medication guidance has been promoted to prevent adverse events for people with chronic conditions. Our aim was to summarize the existing sick day medication guidance and the evidence base for the effectiveness of interventions for implementing this guidance. Study Design: Scoping review of quantitative and qualitative studies. Setting & Population: Sick day medication guidance for people with chronic conditions including diabetes mellitus, kidney diseases, and cardiovascular diseases. Selection Criteria for Studies: A search of 6 bibliographic databases (Ovid MEDLINE, Ovid Embase, CINAHL, Scopus, Web of Science Core Collection, and Cochrane Library [via Wiley]) and a comprehensive gray literature search were completed in June 2021. Data Extraction: Intervention and study characteristics were extracted using standardized tools. Analytical Approach: Data were summarized descriptively, and our approach observed the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping reviews. Results: The literature search identified 2,308 documents, which were screened against the eligibility criteria, leading to 74 documents that were included. The majority of the identified documents (n = 55) were guidelines or educational resources. Of the 19 primary research studies identified, 10 studies described an intervention, with only 2 examining the effect of sick day medication guidance interventions within clinical care and no studies reporting beneficial effects on clinical outcomes. Most documents (n = 58) included guidance specific to patients with diabetes mellitus, with fewer including guidance for patients with chronic kidney disease (n = 9) or heart failure (n = 2). Limitations: Risk of bias was not assessed. Conclusions: Many resources promoting sick day medication guidance have been developed; however, there is very little empirical evidence for the effectiveness of current approaches in implementing sick day medication guidance into practice. Recommendations for the use of sick day medication guidance will require further research to develop consistent, understandable, and usable approaches for its implementation within self-management strategies as well as empirical studies to demonstrate the effectiveness of these interventions.
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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 | 0.041 | 0.126 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".