Polypharmacy and Unplanned Hospitalizations in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Polypharmacy (PP), the prescribing of multiple drugs for an individual, is rising in prevalence. PP associates with an increased risk of adverse drug reactions (ADR) and hospital admissions. We investigated the relationship between PP, characteristics of rheumatoid arthritis (RA), and the risk of unplanned hospital admissions. METHODS: Patients from a hospital RA cohort were retrospectively analyzed. Information was collected from electronic medical records. Cox proportional hazards were used to compare hospitalization risk according to levels of PP. Admissions were adjudicated to determine whether an ADR was implicated. RESULTS: The study included 1101 patients; the mean number of all medications was 5. PP correlated with increasing age, disease duration, disease activity, and disability. At least 1 unplanned admission occurred for 16% of patients. Patients taking ≥ 10 medications had an adjusted HR for hospitalization of 3.1 (95% CI 2.1-4.5), compared to those taking 0-5 medications. Corticosteroid use associated with a doubling in adjusted risk of admission of 1.7 (95% CI 1.2-2.4). The most common reason for hospitalization was infection (28%). While in half of all admissions an ADR was a possible contributing factor, only 2% of admissions were found to directly result from an ADR. CONCLUSION: PP is common in RA and is a prognostic marker associated with increased risk of acute hospitalizations. Our data suggest that PP may be an indicator of comorbidity burden rather than a contributing cause of a drug-related toxicity. PP should be monitored to minimize inappropriate combination of prescribed medications. PP may be a useful predictor of clinical outcomes in epidemiologic studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".