Prescribing cascades: we see only what we look for, we look for only what we know
Bibliographic record
Abstract
Prescribing cascades are increasingly recognized since they were described in the mid-1990s. Cascades are more likely in older people with multimorbidity and associated polypharmacy where multiple medications can induce a variety of side effects that manifest with various non-specific symptoms that may be misidentified as new geriatric syndromes such as falls, dizziness and new-onset incontinence. Geriatricians encounter medication side effects frequently and will usually consider if an older patient presenting with new symptoms could be experiencing an adverse drug reaction or event. However, most medications prescribed to multimorbid older patients are initiated and continued by prescribers without specialist geriatric training who may not detect medication-induced morbidity. Therefore, novel approaches to the detection and management of prescribing cascades in older people are needed. Currently, the knowledge base surrounding prescribing cascades in older people is evolving towards better methods for cascade detection and secondary prevention. However, the large number of cascades described in the literature, the wide-ranging symptomatology of cascades and the rapidly increasing number of multimorbid older people at risk of cascades represent major challenges for prescribers. Furthermore, prospective prevalence studies of prescribing cascades in older people are lacking. To detect and correct prescribing cascades during routine medication review in multimorbid older people, awareness of cascades is essential. Prescribing cascade awareness in turn requires novel explicit ways of defining cascades to facilitate their rapid detection and correction during medication review. Given that prescribing cascades represent another aspect of inappropriate prescribing (IP), explicit cascades criteria should be integrated with other explicit IP criteria.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".