Bibliographic record
Abstract
Born on Merseyside, Andrew Lees qualified in medicine at the Royal London Hospital Medical College in 1970. His neurological training was at University College London Hospitals and the National Hospital for Neurology and Neurosurgery, Queen Square. He also spent 1 year at L’Hopital Salpetriere, Paris. He has achieved international recognition for his work on Parkinson’s disease and abnormal movement disorders. He is an original member of the Highly-Cited Researchers ISI Database with an h-index of 130. Founder member of the international Movement Disorder Society, he was elected President (2004–2006) and co-edited the Movement Disorders Journal between 1995 and 2003. In 2006, he was awarded the Movement Disorders Research Award by the American Academy of Neurology and he was awarded the Association of British Neurologists Medal in 2015. At the age of thirty-two he was appointed to the consultant staff at the National Hospitals, The Middlesex, and Whittington Hospitals and in 1987 was elected a Fellow of the Royal College of Physicians. He was later appointed Professor of Neurology at the National Hospital for Neurology and Neurosurgery, Queen Square and was Director of the Reta Lila Weston Institute for Neurological Studies (1998–2012). He was Clinical Director of the Queen Square Brain Bank for Neurological Disorders (1985–2012) and Director of the Sara Koe PSP Research Centre (2002–2012). He is a Visiting Professor at the University of Liverpool and Queen Mary University of London, and has close collaborations with several Brazilian universities. For his contributions to Brazilian neurology he was elected as an overseas member of the Academia Nacional de Medicina and the Academia Brasileira de Neurologia. He was elected as a Council member of the Academy of Medical Sciences 2012–2015 and appointed as an Expert Adviser for the UK Government National Institute for Health and Care Excellence Centre for Guidelines (2006–2019). He received the Bing Spear Award in 2016 for outstanding contributions towards saner drug policies. He has delivered the Gowers Memorial Lecture at the National Hospital, The Inaugural Lord Brain Memorial Lecture at Bart’s and the Royal London Hospitals and David Marsden Memorial Lecture at the European Federation of Neurological Societies. He was the recipient of Stanley Fahn Lectureship Award at the MDS Dublin 2012, and has been awarded the German Society of Neurology’s 2012 Dingebauer Prize for outstanding scientific attainment in the field of Parkinson’s disease and Neurodegenerative Disorders, the Jay Van Andel award for outstanding research in Parkinson’s disease in 2014, and the Parkinson Canada’s Donald Calne Award and Lectureship for 2017. Through a process of reasoning that left little to the imagination the neurologists at The Royal London Hospital where I trained pulled black swans and zebras from their hats. During my training I was led to understand that it was just a matter of time before all disorders of the brain would be worked out and categorised in terms of anatomical electrical and chemical connections. This rational approach drew me in, and I selected diseases of the nervous system as my specialist subject. My first two neurology chiefs at University College Hospital were inspirational and kind. They warned me that it would take many years to learn how to join up the dots and become proficient at knowing where to look. One of them recommended that I should use textbooks only for reference but that I should read the Sherlock Holmes canon. Over time I came to understand that neurologists and criminal detectives both seek hidden truths and meanings in narrative and that both rely on a rigorous tried and tested method that pays attention to detail. Sherlock Holmes provided a romantic bridge to William Gowers and the serious business of neurology. Clinical research and a curiosity for cures should be an integral part of neurology William Seward Burroughs, who had appeared out of nowhere on the cover of Sergeant Pepper became my unlikely source of inspiration. He introduced me to the idea that nothing happens by chance and that novel scientific discoveries rely heavily on personally distinctive actions. He also helped me to understand that art is a complementary source of truth that enlists inventiveness to transport science beyond the acquisition of fact.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.231 | 0.125 |
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".