The Prevalence of Parkinson Disease in Ukraine
Bibliographic record
Abstract
Reported prevalence of Parkinson's disease (PD) varies significantly among countries.1 To date, little information has been gathered from Eastern Europe.2,3 Crude prevalence rate (CPR) of PD (coded G20 in International Classification of Diseases-10) was collected in 2010 and 2017 from a national database published in Ukraine (Appendix S1: Part A). 4 As Ukraine is divided into 25 regions with additional data for the cities of Kyiv and Sevastopol, there were 27 data points in 2010.The Autonomous Republic of Crimea (including Sevastopol city) was temporarily occupied in 2014 so in 2017, only 25 data points were collected.National information has not been available since 2018 because of reorganization of the healthcare system.However, adult prevalence data from 2019 to 2020 was obtained from the Kyiv region (excluding Kyiv city) (personal communication Dr. Anatoly Galusha, Chief Neurologist of Kyiv region).The overall CPR of PD in Ukraine was 59.6 per 100,000 in 2010, and rose to 67.5 per 100,000 by 2017.Significant geographic differences were seen (Fig. 1, Appendix S1: Table S1).The latest CPR in Ukraine remains lower than the rate reported by other eastern European countries collected over a similar time period, which range from 93.3 to 404/100,000.2,3 We suspect that underdiagnoses and unequal access to healthcare remain determinants of the relatively low Ukrainian prevalence (Appendix S1: Part B).Recent data from the Kyiv region revealed the prevalence of PD in adults (age, 18-100 years-old) was 97/100,000 in 2019, and decreased to 77/100,000 by the end of 2020.Although the Kyiv region previously recorded a higher prevalence in 2017, rates were lower in 2019 and 2020.The reasons for this are still unclear, but deaths and lower accessibility to medical care related to the coronavirus disease 2019 (COVID-19) pandemic could have played a role.FIG. 1. Political map of Ukraine (adapted from MapChart, https://www.mapchart.net/ukraine.html)with respective prevalence (per 100,000) in each region in 2010 (A) and 2017 (B).Modified from Hobzei et al. 4 *Data may not be accurate due to becoming an autonomous region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".