Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
What initially sparked your interest in cardiology, and what continues to motivate you in this field to this day?I began my residency in cardiology at Bichat Hospital in Paris, France.At that time, it was one of the leading institutions for valvular heart disease, under the supervision of Alec Vahanian, who was also heading the European Guidelines for Valvular Heart Disease.The team included many brilliant colleagues, such as Bertrand Cormier, who developed a dedicated classification to predict procedural results after percutaneous mitral valve commissurotomy in patients with mitral stenosis, Bernard Lung, and David Messika-Zeitoun, who is now working in Ottawa, Canada, and has become my mentor.I was privileged to meet these people, discover this institution, and learn a lot from them.Working with passionate individuals, their enthusiasm becomes contagious, and it probably started for me at that time.I consider myself truly lucky.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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 it