Bibliographic record
Abstract
The British Cardiovascular Society (BCS) 2019 Annual Conference was held in Manchester, UK, on 3–5 June and was attended by over 2000 cardiologists, trainees, and allied healthcare professionals. Spread over 3 days, the conference theme was Digital Health Revolution giving delegates an opportunity to explore how healthcare is rapidly changing in the digital era and to examine the challenges and opportunities that new technologies will place on improving patient care. The conference was opened by Dr Robert Califf giving the Keynote lecture entitled The Cycle of Evidence Generation and Consumption in the 4th Industrial Revolution. Dr Califf is a former Commissioner of the Food and Drug Administration, Professor of Medicine and Vice Chancellor for Clinical and Translational Research at Duke University, and founding director of the Duke Clinical Research Institute. His lecture highlighted how in our current 4th industrial revolution, new technologies are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human. This was the perfect opening to the conference, which then followed with sessions on Big Data, Artificial Intelligence, Machine Learning and Digital Security, across all different aspects of cardiovascular care.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.045 | 0.019 |
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".