A Systematic Review of Studies of the STOPP/START 2015 and American Geriatric Society Beers 2015 Criteria in Patients ≥ 65 Years
Bibliographic record
Abstract
BACKGROUND: Polypharmacy remains problematic for individuals ≥65. OBJECTIVE: To summarise the percentages of patients meeting 2015 STOPP criteria for Potentially Inappropriate Prescriptions (PIPs), 2015 Beers criteria for Potentially Inappropriate Medications (PIMs), and START criteria Potential Prescribing Omissions (PPOs). METHODS: Searches conducted on 2 January 2019 in Medline, Embase, and PubMed identified 562 studies and 62 studies were retained for review. Data were abstracted independently. RESULTS: 62 studies (n=1,854,698) included two RCTs and 60 non-randomised studies. For thirty STOPP/START studies (n=1,245,974) average percentages for ≥1 PIP weighted by study size were 42.8% for 1,242,010 community patients and 51.8% for 3,964 hospitalised patients. For nineteen Beers studies (n = 595,811) the average percentages for ≥1 PIM were 58% for 593,389 community patients and 55.5% for 2,422 hospitalised patients. For thirteen studies (n=12,913) assessing both STOPP/START and Beers criteria the average percentages for ≥1 STOPP PIP were 33.9% and Beers PIMs 46.8% for 8,238 community patients, and for ≥ 1 STOPP PIP were 42.4% and for ≥1 Beers PIM 60.5% for 4,675 hospitalised patients. Only ten studies assessed changes over time and eight found positive changes. CONCLUSION: PIP/PIM/PPO rates are high in community and hospitalised patients in many countries. RCTs are needed for interventions to: reduce new/existing PIPs/PIMs/PPO prescriptions, reduce prescriptions causing adverse effects, and enable regulatory authorities to monitor and reduce inappropriate prescriptions in real time. Substantial differences between Beers and STOPP/START assessments need to be investigated whether they are due to the criteria, differential medication availability between countries, or data availability to assess the criteria.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".