Determining the impact of specialized care on health outcomes and health care utilization in Parkinsonism
Bibliographic record
Abstract
BACKGROUND: Although care of Parkinsonism (PKM) is assumed to be optimally provided by movement disorder neurologists within an interdisciplinary clinic model, there is a paucity of published data to support this. OBJECTIVES: To investigate the impact of movement disorder neurologist care of individuals with Parkinsonism (PKM). METHODS: A retrospective exposure design was adopted using administrative data. Incident PKM individuals were identified in billing claims. A nine-year exposure period to movement disorder neurologist, general neurologist and non-neurologist care was calculated based on the billing codes. Regression models were used to test the association of provider exposure on time to death and long-term care (LTC) admission. Linear models were used to test varying provider exposure and hospital admissions, hospital days and emergency department visits. RESULTS: 1914 incident individuals were identified. There was no difference in PKM mortality, emergency visits, hospital admissions, or hospital days between providers, however exposure to general neurology and non-neurology care was associated with a significantly higher risk of admission to LTC compared to movement disorder neurologist care (HR 1.43; 95% CI 1.09-1.87 for general neurology (p-value = 0.0089); HR 1.61; 95% CI 1.25-2.05 for non-neurology (p-value = 0.0002), respectively. CONCLUSION: Movement disorder neurologist care is associated with a lower risk of admission to LTC over general neurologist care in individuals with PKM.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".