Prescribing Cascades with Recommendations to Prevent or Reverse Them: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: To reduce prescribing cascades occurring in clinical practice, healthcare providers require information on the prescribing cascades they can recognize and prevent. OBJECTIVE: This systematic review aims to provide an overview of prescribing cascades, including dose-dependency information and recommendations that healthcare providers can use to prevent or reverse them. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was followed. Relevant literature was identified through searches in OVID MEDLINE, OVID Embase, OVID CINAHL, and Cochrane. Additionally, Web of Science and Scopus were consulted to analyze reference lists and citations. Publications in English were included if they analyzed the occurrence of prescribing cascades. Prescribing cascades were included if at least one study demonstrated a significant association and were excluded when the adverse drug reaction could not be confirmed in the Summary of Product Characteristics. Two reviewers independently extracted and grouped similar prescribing cascades. Descriptive summaries were provided regarding dose-dependency analyses and recommendations to prevent or reverse these prescribing cascades. RESULTS: A total of 95 publications were included, resulting in 115 prescribing cascades with confirmed adverse drug reactions for which at least one significant association was found. For 52 of these prescribing cascades, information regarding dose dependency or recommendations to prevent or reverse prescribing cascades was found. Dose dependency was analyzed and confirmed for 12 prescribing cascades. For example, antipsychotics that may cause extrapyramidal syndrome followed by anti-parkinson drugs. Recommendations focused on dosage lowering, discontinuing medication, and medication switching. Explicit recommendations regarding alternative options were given for three prescribing cascades. One example was switching to ondansetron or granisetron when extrapyramidal syndrome is experienced using metoclopramide. CONCLUSIONS: In total, 115 prescribing cascades were identified and an overview of 52 of them was generated for which recommendations to prevent or reverse them were provided. Nonetheless, information regarding alternative options for managing prescribing cascades was scarce.
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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 | 0.031 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".