Health Care Utilization in the Last Year of Life in Parkinson Disease and Other Neurodegenerative Movement Disorders
Bibliographic record
Abstract
Background and Objectives: Neurodegenerative movement disorders are rising in prevalence and are associated with high health care utilization. Generally, health care resources are disproportionately expended in the last year of life. Health care utilization by those with neurodegenerative movement disorders in the last year of life is not well-understood. The goal of this study was to assess the utilization of acute care in the last year of life among individuals with neurodegenerative movement disorders and determine whether outpatient neurology or palliative care affected acute care utilization and place of death. Methods: We conducted a retrospective cross-sectional study including health system administrative data in Alberta, Canada, from 2011 to 2017. Administrative data were used to determine place of death and quantify emergency department (ED) visits, hospitalizations, intensive care unit admissions, and outpatient generalist and specialist visits. Diagnoses were classified by 10th revision of the International Classification of Diseases codes. Stata 16v was used for statistical analyses. Results: < 0.001). Discussion: There are high rates of in-hospital death and acute care utilization in the year before death among those with neurodegenerative movement disorders. Most did not access specialist palliative or neurologic care in the last year of life. Outpatient palliative care and home care services were associated with increased odds of dying at home. Our results indicate the need for further research into the causes, costs, and potential modifiers to inform public health planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".