Health Economics and Surgical Treatment for Parkinson's Disease in a World Perspective: Results from an International Survey
Bibliographic record
Abstract
BACKGROUND: Most studies in the field of neurosurgical treatment for movement disorders have been published by a small number of leading centers in developed countries. This study aimed to investigate the clinical practice of stereotactic neurosurgery for Parkinson's disease (PD) worldwide. METHODS: Neurosurgeons were contacted via e-mail to participate in a worldwide survey. The results obtained are presented in order of the countries' economic development according to the World Bank, as well as by the source of financial support. RESULTS: A total of 353 neurosurgeons from 51 countries who had operated on 13,200 patients in 2009 were surveyed. Surgical procedures performed in high-income countries were more commonly financed by a public health care system. In contrast, in lower-middle-income and upper-middle-income countries, patients frequently financed surgeries themselves, and ablative surgeries were most commonly performed. Unexpectedly, ablative surgery is still used by about 65% of neurosurgeons, regardless of their country's economic status. CONCLUSIONS: This study provides a previously unavailable picture of the surgical aspects of PD across the globe in relation to health economics and sociodemographic factors. Global educational and training programs are warranted to raise awareness of economically viable surgical options for PD that could be adopted by public health care systems in lower-income countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".