What Is Known About Preventing, Detecting, and Reversing Prescribing Cascades: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: To systematically describe the resources available on preventing, detecting, and reversing prescribing cascades using a scoping review methodology. MEASUREMENTS: We searched Medline, EMBASE, PsychINFO, CINAHL, Cochrane Library, and Sociological Abstracts from inception until July 2017. Other searches (Google Scholar, hand searches) and expert consultations were performed for resources examining how to prevent, detect, or reverse prescribing cascades. We used these three categories along the prescribing continuum as an organizing framework to categorize and synthesize resources. RESULTS: Of 369 resources identified, 58 met inclusion criteria; 29 of these were categorized as preventing, 20 as detecting, and 9 as reversing prescribing cascades. Resources originated from 14 countries and mostly focused on older adults. The goal of preventing resources was to educate and increase general awareness of the concept of prescribing cascades as a way to prevent inappropriate prescribing and to illustrate application of the concept to specific drugs (e.g., anticholinergics) and conditions (e.g., inflammatory bowel disease). Detecting resources included original investigations or case reports that identified prescribing cascades using health administrative data, patient cohorts, and novel sources such as social media. Reversing prescribing cascade resources focused on the medication review process and deprescribing initiatives. CONCLUSION: Prescribing cascades are a recognized problem internationally. By learning from the range of resources to prevent, detect, and reverse prescribing cascades, this review contributes to improving drug prescribing, especially in older adults. J Am Geriatr Soc 66:2079-2085, 2018.
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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 | 0.038 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.036 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".