What Is Known About Preventing, Detecting, and Reversing Prescribing Cascades: A Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it