Assessing the Scope and Appropriateness of Prescribing Cascades
Bibliographic record
Abstract
As originally defined, the term "prescribing cascade" describes a sequence of events that begins when an adverse drug event (ADE) occurs, is misinterpreted as a new medical condition, and a subsequent drug is then inadvertently prescribed to treat the new condition. We refine the definition to encompass both recognized and unrecognized ADEs because they can both contribute to problematic prescribing practices. In addition, we discuss that although prescribing cascades are most commonly viewed as problematic, they may be appropriate and therapeutically beneficial in certain clinical situations. We differentiate between appropriate and problematic prescribing cascades by adopting a similar approach to the framework proposed in the highly acclaimed King's Fund report Polypharmacy and Medicines Optimization. Practical considerations are also presented to aid clinicians in preventing the propagation of problematic prescribing cascades within their clinical practice. Providing new perspectives on the scope and appropriateness of the prescribing cascade concept is an important step in describing clinically relevant cascades and in encouraging safe prescribing practices. J Am Geriatr Soc 67:1023-1026, 2019.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".