Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Polypharmacy is a growing concern in geriatric medical care, increasing the risk of adverse drug events (ADEs), cognitive decline, falls, and hospitalizations. Multiple prescriptions result in potentially inappropriate medications (PIMs), leading to a prescribing cascade and medication overload for many patients. Numerous medications are deemed unnecessary or not appropriate, decreasing the quality of life. The lack of standardized deprescribing tools in clinical practice contributes to continued polypharmacy-related complications. The " project, was implemented at a PACE (Program of All-Inclusive Care for the Elderly) facility to improve medication management, patient safety, and quality of life through structured deprescribing interventions. Methods: This quality project utilized tools such as MedStopper, Beers Criteria, and STOPP/START guidelines to identify and reduce PIMs over an 8–10-week period. The Knowledge-to-Action (KTA) Framework and the Plan-Do-Study-Act (PDSA) model guided implementation. Results: The deprescribing intervention led to significant improvement in medication management, patient outcomes, reduced medication burden, improved cognitive function, and decreased adverse drug events. Conclusion: Deprescribing is a valuable quality improvement method for managing polypharmacy in older adults. This project demonstrates that structured deprescribing can be effectively used in elderly care, aligning with the current Centers for Medicare & Medicaid Services (CMS), 4Ms Framework to provide holistic, patient-centered care. Future efforts should focus on developing user-friendly deprescribing tools that integrate with electronic health records (EHRs), enhance provider training and awareness, continue research on long-term deprescribing outcomes, and improve accessibility for U.S. and international providers. I encountered several deprescribing programs currently available solely to Australian, New Zealand and Canadian residents.
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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it