Introduction to the Special Issue on dopamine celebrating the 90th birthday of Oleh Hornykiewicz
Bibliographic record
Abstract
More than five decades ago, Oleh Hornykiewicz described reduced striatal dopamine levels in postmortem brains of patients with Parkinson's disease and subsequently developed what still is the first-line treatment today, the restoration of deficient dopamine levels by administering its precursor, L-DOPA. It is probably safe to say that this was one of the major breakthroughs in the history of modern medicine; it has transformed the lives of tens of millions of people with Parkinson's and countless family members and carers. It still serves as one of the most vivid examples of the successful translation of basic research into clinical practice. In 2016, Oleh Hornykiewicz celebrated his 90th birthday. As a tribute to his pioneering work, the Dopamine 2016 conference was held in Vienna, Austria, where Oleh Hornykiewicz spent important parts of his scientific career and where he is still working today. We included in the programme a symposium entitled ‘Oleh Hornykiewicz Special Birthday Symposium’ which was sponsored by EJN and FENS. The symposium was chaired by Robert Schwarcz and included Ann Graybiel, Michael Schlossmacher and Werner Poewe as speakers. Oleh Hornykiewicz at the Cocktail Reception in Vienna Town Hall following the ‘Oleh Hornykiewicz Special Birthday Symposium’ at Dopamine 2016 (Monday, 5th September 2016). Oleh Hornykiewicz with his wife, Christine, holding a letter from Canadian Prime Minister, Justin Trudeau, congratulating him on his life's work on Parkinson's disease and on the occasion of his 90th birthday.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.061 | 0.037 |
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".