Bibliographic record
Abstract
Pathophysiology 1. Is it all plaque rupture? Davies, London, UK 2. Plaque vulnerability and ACS: what are the prophylactic and therapeutic implications? Schneider and Sobel, Colchester/Burlington, USA 3. What is the role of infection on pathogenesis? Kaski and Smith, London, UK 4. What is the role of coronary tone? Uren, Edinburgh, UK 5. What is known about the genetics of ACS? Samani and Singh, Leicester, UK Diagnosis 6. What is the role of advanced electrocardiology? Dellborg, Sweden 7. Biochemical tests in suspected ACS - which test when? Collinson, London, UK 8. What is the role of MRI? Cherryman and Sensky, Leicester, UK 9. What is the role of PET? Camici and Spinks, London, UK 10. Does stress testing have a role? Madsen, Copenhagen, Denmark 11. What is the role of echocardiography? Nihoyannopoulos, London, UK Treatment 12. Which heparin and for how long? Anand and Hirsh, Ontario, Canada 13. What about the novel anti-platlet agents? Verheught, Nijmegen, Netherlands 14. What is the value of novel thrombolytic drugs and combination therapies? Tiefenbrunn, St Louis, USA 15. Pre-conditoning and acute coronary syndromes? Yellon and Bell, London, UK 16. Is myocardial protection effective? Bergmann, USA 17. Angioplasty and stenting in acute coronary syndromes? Topol, L'Allier and Ellis, Cleveland, USA
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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.054 | 0.027 |
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".