Using a clinical process map to identify prescribing cascades in your patient
Bibliographic record
Abstract
### What you need to know The adverse effects of a drug may “hide” behind common presenting symptoms.1 Prescribing cascades occur when a healthcare provider misinterprets an adverse drug event as a new medical condition and provides a second drug to address the side effect, as described in 1997 by Rochon and Gurwitz in the BMJ .2 The concept has also been expanded to include unnecessary diagnostic tests, medical devices, and over-the-counter therapies,3 which may expose the patient to risk and harm. There is no universally accepted approach to identifying whether a drug is responsible for a symptom, but by identifying prescribing cascades, clinicians can reduce the number of unnecessary medications, investigations, consultations, and harms. To date, more than 20 prescribing cascades have been identified by cohort and population studies4 (see fig 1 for examples). These prescribing cascades are the result of medications for common conditions such as heart disease, hypertension, obstructive lung disease, diabetes, dementia, and chronic pain.356 Fig 1 Simple clinical process maps of three common prescribing cascades While prescribing cascades are well …
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".