British Cardiovascular Society/British Heart Foundation/British Atherosclerosis Society/British Society for Cardiovascular Research Young Investigator Award 2023
Bibliographic record
Abstract
The British Cardiovascular Society (BCS)/British Heart Foundation (BHF)/British Atherosclerosis Society/British Society for Cardiovascular Research Young Investigator Award was established in 2001 to recognise excellence in young researchers intending to pursue a career in cardiovascular clinical medicine or research. It is open to both young clinicians and basic scientists. Clinicians should not have attained consultant status at the time the research was performed and basic scientists should be no more than 5 years post-PhD. Investigators submit an abstract, limited to 1000 words, which is then reviewed, and five finalists are selected to give a 10-minute presentation during the BCS annual conference in Manchester, UK. This is followed by five minutes of questions by the judges. The profiles and presentation summaries of this year’s winner, Dr Sau, and the other finalists are shown below. ![Graphic][1] Dr Arunashis Sau is a clinical research fellow, cardiology registrar and PhD candidate at Imperial College London. His main research interest is the application of machine learning (ML) to further the field of cardiology, including applying deep learning to the surface ECG and to intracardiac electrograms. He studied medicine at Imperial College London, where he was awarded a First Class (Honours) degree in Medical Sciences with Cardiovascular Sciences. His postgraduate clinical training to date has been in London, most recently as a National Institute for Health and Care Research (NIHR) Academic Clinical Fellow. He has been awarded a BHF Clinical Research Training Fellowship and started this in October 2021 under the primary supervision of Dr Fu Siong Ng. Dr Sau’s Young Investigator Award presentation began with the hypothesis that ML has the potential to identify novel markers of risk from the ECG that can go beyond clinician ECG interpretation. In the course of supervised ML training, many thousands of ECG features are identified which are not limited to conventional … [1]: /embed/inline-graphic-1.gif
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.389 | 0.304 |
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".