Polypharmacy of potentially addictive medication in the older persons—quantifying usage
Bibliographic record
Abstract
PURPOSE: The use of restricted medications, for example, opioids, benzodiazepines (BZD), and z-hypnotics, in the older persons continues to increase. Little is known about usage practices or about the extent of polypharmacy within this group. The objectives of this study were (i) to describe polypharmacy and (ii) to develop a medication usage index (MUI) to quantify usage patterns. METHODS: Data for 2008 were obtained from the Norwegian Prescription Database containing all prescriptions filled in Norwegian pharmacies. The study population included people aged 70-89 years who filled prescriptions for weak opioids, strong opioids, anxiolytic BZD, hypnotic BZD, and/or z-hypnotics. A MUI was developed based on Anatomical Therapeutic Chemical codes, defined daily doses, Anatomical Therapeutic Chemical subgroups, and number of prescribers. RESULTS: Forty-two percent of elderly Norwegians filled at least one prescription in one of the medication subgroups in 2008. MUI Level 1 (least) usage was shown by 56.6% of users (23.8% of the general population), Level 2 by 29.7% (12.5%), Level 3 by 11.3% (4.8%), and Level 4 (most) by 2.4% (1.0%). People using strong opioids were the most likely to use other restricted medications. In addition, female participants had a higher MUI than did male participants, and older users higher than younger users. Cancer or palliative care patients attained twice the MUI points than did the others. CONCLUSIONS: Polypharmacy was found to be common within these restricted drug categories for the older persons. MUI provides a convenient approach to summarizing drug usage and will be useful in detecting trends and regional differences and determining the impact of interventions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".