Polypharmacy, the Good Prescribing Continuum, and the Ethics of Deprescribing
Bibliographic record
Abstract
I will apply, for the benefit of the sick, all measures [that] are required, avoiding those twin traps of overtreatment and therapeutic nihilism. –Excerpt from Hippocratic Oath–Modern Version, written in 1964 by Louis Lasagna, Academic Dean of the School of Medicine at Tufts University Older adults are being prescribed increasing numbers of medications. For example, in 2000, 24% of Americans 65 years of age or older used 5 or more prescription drugs. By 2012, that percentage had risen to 39% (Kantor, Rehm, Haas, Chan, & Giovannucci, 2015). Levels of medication use are even higher among older adults residing in assisted living and nursing home settings. The complexity of drug regimens to treat specific conditions in older patients has also continued to increase over time. Therapeutic regimens that include the use of two or more different medications to treat a single condition are increasingly promoted for the optimal management of conditions that are prevalent in the older patient population, including hypertension, heart failure, ischemic heart disease, diabetes mellitus, and Alzheimer’s disease. Promotion of prescription drugs directly to consumers also factors into increased levels of medication prescribing to older persons, with advertising focusing heavily on conditions common in older adults, such as arthritis, hyperlipidemia, diabetes mellitus, heart disease, depression, and Parkinson’s disease (Greenway & Ross, 2017).
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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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".