Polypharmacy Among the Elderly in a List-Patient System
Bibliographic record
Abstract
BACKGROUND: Polypharmacy, i.e. the simultaneous use of multiple drugs, is known to be associated with compliance errors and adverse drug reactions. Norway has a list-patient system in general practice, formalizing the relationship between the patient and his/her regular general practitioner (GP). One important aim with a list-patient system is to secure medical quality in primary care by giving the GP the responsibility for coordinating the medical treatment. OBJECTIVE: To examine the regular GP's role in polypharmacy to the home-dwelling elderly in Norway and to determine by how much multiple prescribers increase the risk of polypharmacy. METHODS: This was a population registry study based on data on all prescription drugs dispensed at pharmacies to patients 70 years and older from the Norwegian Prescription Database, merged with data on GPs and GPs' patient lists from the Regular General Practitioner Database. The dataset included 624,308 patients and 4520 GPs in the period from 2004 to 2007. Outcome measures were: number of drug-substances prescribed and dispensed per patient by the regular GP, other GPs, non-GP specialists and hospital doctors; polypharmacy, defined as five or more prescribed and dispensed substances in the same quarter; excessive polypharmacy, defined as ten or more prescribed and dispensed substances in the same quarter. RESULTS: Polypharmacy is high and increasing despite the list-patient system. GPs prescribe all the substances that cause polypharmacy in 64 % of the incidents, but the patients' risk of polypharmacy increases substantially with number of prescribers, odds ratio 2.32 (95 % CI 2.31-2.33). CONCLUSION: GPs have a major role in the high and increasing polypharmacy among the elderly in Norway. Any intervention intending to improve the situation must necessarily include the GPs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".