“Kind of blurry”: Deciphering clues to prevent, investigate and manage prescribing cascades
Bibliographic record
Abstract
BACKGROUND: Prescribing cascades, where a medication is used to treat the side effect of another medication, contribute to polypharmacy and related morbidity. Little is known about clinicians' and patients' experiences with prescribing cascades. In this study, we explored why and how prescribing cascades occur across a variety of care settings and how they are managed. METHODS AND FINDINGS: This descriptive qualitative study employed semi-structured interviews with older adults who may have experienced a prescribing cascade(s), their caregivers, and healthcare providers. Interviewees were recruited through physician referral from a Geriatric Day Hospital, two long-term care homes in Ottawa, Ontario, and through self-referral across Ontario, Canada. An inductive approach was used to code data and determine themes. Thirty-one interviews were conducted for ten unique patient cases. Some interviewees were involved in more than one case, resulting in 22 unique interviewees. Three themes were identified. First, recognition of prescribing cascades is linked to awareness of medication side effects. Second, investigation and management of prescribing cascades is simultaneous and iterative (rather than linear and sequential). Third, prevention of prescribing cascades requires intentional strategies to help people anticipate and recognize medication side effects. Difficulty with recruitment from both long-term care homes and through self-referral was the central limitation. This exemplifies challenges associated with studying a poorly recognized and underexplored phenomenon. CONCLUSIONS: In order to better recognize, investigate and manage prescribing cascades, clinicians and patients need to know more about medication side effects; they need to ask 'can this be caused by a drug?' when signs and symptoms arise or worsen; and they need access to information about medication experiences to have benefit-risk discussions and make decisions about deprescribing. Approaches for raising public awareness of prescribing cascades should be trialed to raise the profile of this issue and facilitate continued exploration of the phenomenon.
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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 | 0.027 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".