Évaluation des capteurs plans en imagerie cardiaque interventionnelle
Bibliographic record
Abstract
Les systemes d’angiographie cardiaque doivent permettre aux cardiologues interventionnistes de quantifier avec precision le degre de stenose et de voir en temps reel de fines structures en mouvement telles que les arteres, les catheters ou les stents. La dose au patient peut etre relativement importante selon la duree des procedures. La qualite d’image etant directement reliee a la dose, l’enjeu technologique du systeme est d’offrir la meilleure qualite d’image a un taux d’exposition le plus bas possible. L’integration de la technologie des detecteurs plats en angiographie cardiaque semble apporter une amelioration importante de la qualite d’image par rapport aux traditionnels amplificateurs de brillance et cameras, avec cependant, des taux d’exposition plus eleves. Quels sont les reels avantages de ces systemes qui tendent a s’imposer sur le marche? Apres une description de la technologie des detecteurs plats et leur integration au systeme d’imagerie cardiaque, nous presentons leurs performances en terme de qualite d’image, d’exposition et de quantification automatique des arteres. Ces resultats sont obtenus avec le fantome de fluoroscopie cardiovasculaire NEMA/SCA&I que nous presentons ici comme moyen standardise de caracteriser les systemes d’angiographie cardiaques
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".