Variations in hospitalization rates across Parkinson's Foundation Centers of Excellence
Bibliographic record
Abstract
INTRODUCTION: Patients with Parkinson's disease (PD) are at increased risk for hospitalization and often experience worsening of PD when hospitalized. It is therefore important to identify strategies to prevent hospitalization. METHODS: Hospital encounter rates in different Parkinson's Foundation Centers of Excellence in United States, Canada, Israel and the Netherlands were analyzed as part of the Parkinson Foundation Parkinson's Outcomes Project (PF-POP). Multivariate logistic regression was used to estimate the odds ratio for hospitalization, adjusted for risk factors. RESULTS: Baseline age, disease duration, other relative than spouse/partner as care giver, cancer, arthritis, other comorbidities, falls, use of levodopa, use of dopamine agonist, use of COMT inhibitor, occupational therapy before the baseline visit, PDQ-39, MSCI total score and time between visits were significantly associated with the risk of hospital encounters. After adjustment for these factors, two centers had significantly lower odds for hospitalization admission and ER visit (minimum OR 0.3) and four centers had significantly higher odds (maximum OR 1.5) than the average center. Four centers had significantly lower hazard ratios for time to re-hospitalization compared to the average center. Reducing hospital admission rates in those centers with higher than average rates would reduce overall hospitalizations by 11%. Applied to PD patients over 65 nationwide this represents a potential for cost savings of greater than $1 billion over 48 months. CONCLUSION: Encounter rates vary even across expert centers and suggest that practices carried out in some centers may reduce the risk of hospitalization. Further research will be necessary to identify these practices and implement them more widely to improve care for people with PD.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".