Assessing Medication Problems in those ≥ 65 Using the STOPP and START Criteria
Bibliographic record
Abstract
BACKGROUND: Polypharmacy is a key problem for those ≥65. OBJECTIVE: To summarise for individuals ≥65 the rates of Potentially Inappropriate Medications (PIMs) identified by application of STOPP, and Potential Prescribing Omissions (PPOs) by START criteria. METHODS: Search: Databases were searched 1980 to 1 December 2015. For Medline the search yielded 3,691 systematic reviews or meta-analyses and 301 when limited to 65 years and over. STOPP.mp yielded 180 citations, START.mp 109,132 and 105 when limited to both. For Embase the search yielded 24,681 systematic reviews or meta-analyses, and 881 when limited to 65+ years. STOPP.mp yielded 427 citations and START.mp 147,322, and 327 when limited to both. RESULTS: Search: identified 28 studies with data and plus a systematic review using STOPP/START criteria. For community dwelling-individuals for national outpatient databases (n=1,528,785) PIMs weighted average was 31%, PPOs 47%. For small community studies (n=2,228) PIMs weighted average was 26%, PPOs 24%. For hospitalised patients (n=4,237) PIMs weighted average was 47%, PPOs 50%. For nursing home patients PIMs weighted average (n=1,539 patients) was 59%, PPOs (n=463 residents) 49%. Principal PIMs were benzodiazepines, proton pump inhibitors, NSAIDs, aspirin, and duplicate medications. Principal PPOs were omissions of medications for cardiovascular diseases, hypertension, osteoporosis, diabetes and hyperlipidemia. CONCLUSIONS: Rates of PIMs and PPOs are high. Criteria are currently based on expert consensus. Next steps are to link criteria to the best internationally-accepted evidence-based systematic reviews/guidelines and conduct RCTs to test whether application of the criteria leads to lower rates of medication errors and hospital admissions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".