Peter Sleight, Emeritus Professor of Cardiovascular Medicine, Oxford University, Chairman of the ISIS Trials Steering Committee
Bibliographic record
Abstract
Professor Peter Sleight born 27 June 1929 died peacefully aged 91 on 7 October 2020 and will be remembered as one of the most distinguished and influential cardiologists of his generation Peter was born in Hull and studied medicine at Gonville and Caius College, Cambridge and St Bartholomew’s Hospital, London, where he met his wife and life-long partner Gillian. He qualified as a doctor in 1953 and undertook compulsory National Service with the Royal Air Force (RAF) in 1954 before working as a junior doctor in London. He developed an interest in cardiology after meeting the inspirational Paul Wood at the Brompton Hospital and developed an interest in auscultation of the heart while working for Aubrey Leatham at St George’s Hospital. Clinical posts in London’s teaching hospitals and Harley Street beckoned but Peter’s career changed course when he was advised to undertake a period of research in America. In 1961, he moved to San Francisco to work with Julius Comroe and Maurice Sokolow and would later describe this experience as a career and life-changing event. Within a matter of months, Peter had discovered the ventricular receptors that mediate hypotension and bradycardia. He was awarded the 1962 Young Investigator Award of the American College of Cardiology for this work and went on to publish a series of classic papers on cardiovascular reflexes in the Journal of Physiology.
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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 | 0.051 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.044 | 0.024 |
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".