Antiparkinson drug use in response to practice parameter publication, drug availability, and ‘unofficial’ prescribing forces (P4.149)
Bibliographic record
Abstract
OBJECTIVE: To describe patterns of antiparkinson drug utilization between January 2001 and December 2012 in a national cohort of individuals with Parkinson disease (PD). We also examined the impact of practice parameter publication, drug introduction/withdrawal, and ‘unofficial’ prescribing forces on prescribing patterns. BACKGROUND: Therapeutic options and practice parameters for PD have changed significantly in the past 15 years, yet prescribing practices in the U.S. are unknown. DESIGN/METHODS: Retrospective study of 16,785 individuals receiving pharmacological treatment for PD who were identified in Cerner Health Facts®. Our primary outcome was standardized annual prevalence of antiparkinson drug use by drug class from 2001 to 2012. We also compared antiparkinson medication trends and polypharmacy by age and sex. RESULTS: The most frequently prescribed PD drugs between 2001 and 2012 were levodopa (83[percnt]) and dopamine agonists (29[percnt]). Dopamine agonist use began to fall in 2007, from 37[percnt] to 25[percnt] in 2012, but the timing of the decline was more closely associated with adverse event reporting rather than publication of the American Academy of Neurology’s (AAN) practice parameter refuting levodopa toxicity. Despite safety concerns for cognitive impairment and falls, individuals 蠅80 years had stable rates (20[percnt]) of dopamine agonist use. Polypharmacy was most common in younger individuals. CONCLUSIONS: Dopamine agonist use declined from 2007 to 2012, suggesting that increased awareness of safety issues and AAN practice parameters influenced prescribing. However, these events seemed to have little effect on the treatment provided to older adults with PD. Additionally, use of dopamine agonists did not begin to decline until two years after publication of the AAN’s practice parameter, showing that the decline in dopamine agonist use was not immediate and may have been impacted by increasing safety concerns.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".