How are prescribing cascades defined in the literature? A scoping review
Bibliographic record
Abstract
INTRODUCTION: Prescribing cascades are important medication-related issues to be aware of, particularly for multimorbid older adults with polypharmacy. These cascades were initially defined as phenomena where side effect misinterpretation results in the prescribing of additional medications. This definition has been debated though, particularly on whether side effects may be misinterpreted or recognised/unrecognised, and consequently whether cascades are intentional/unintentional. Given these inconsistencies, this scoping review aimed to map how prescribing cascades have been defined and described in the published literature. METHODS: Seven electronic databases were searched from inception to October 2024. Full-text publications in English that mentioned prescribing cascade (or a synonym) in the title or abstract and provided a prescribing cascade definition/description in the full text were included. Specific terminology and images used to define/describe prescribing cascades were extracted, and the findings were narratively synthesised. RESULTS: Of the 139 included publications, less than half directly aligned with the original definition by containing descriptions of prescribing cascades that indicated side effect misinterpretation (48.9%). One quarter indicated side effects could be recognised or unrecognised (24.5%), 37.4% addressed cascade appropriateness or inappropriateness, and 8.6% referenced their intentional or unintentional nature. One fifth (20.9%) included an image or map to describe a prescribing cascade. CONCLUSION: This review has uniquely mapped how prescribing cascades have been described in the literature, finding substantial heterogeneity between publications. By highlighting this inconsistent terminology use, this review emphasises the need to develop consensus definitions to aid in the future recognition, measurement, education, and prevention of prescribing cascades.
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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 | 0.044 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.045 | 0.039 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".