International Academy of Cardiology 18th World Congress on Heart Disease Annual Scientific Sessions 2013. Vancouver, B.C., Canada, July 26-29, 2013
Bibliographic record
Abstract
Objectives \nWe assessed the feasibility of CartoSoundTM technology (Biosense \nWebster Inc, Diamond Bar, CA) to image the three-dimensional (3D) relationships of fibrotic binding sites between leads and the \ncardiovascular system during lead extraction. \nBackground \nFibrous adherences are the principal cause of permanent cardiac \npacing lead failed removal and complications, and are not directly \nvisualized by standard approach. \nMethods and Results \nSegments of real-time 2D ultrasound images were acquired using a 10-Fr 3D SoundStarTM catheter and integrated into the Carto mapping system to obtain 3D CartoSound anatomical maps of the superior vena cava, right atrium (RA), coronary sinus, right ventricle (RV), pacing leads, and fibrous tissue during lead removal. Lead extraction procedure was performed on 46 patients (38 men; mean age 73.7±10.5 years), and 90 leads (1.96 leads/patient) with a mean time from implant of 62.7±51.8 months. CartoSound was able to detect more binding sites in RA (17.4% vs. 4.3%, p=.04), and RV (43.5% vs. 21.7%, p=.04) compared to fluoroscopy. Mean fibrosis volume (mean 2.0±1.6 cm3) correlated positively with time from implant (r=.38, p<.05), and powered-sheaths use (r=.39, p<.05), and negatively with procedural success (r=-.37, p<.05). Mean CartoSound evaluation time was 4.9±2.3 min. When compared to standard approach, the CartoSound use was characterized by a significantly lower mean procedure time (99±35.5 min vs. 30.1±23.2 min, p=.001), and major complications (1.7% vs. 0%, p=.03). \nConclusions \nReal-time 3D fibrosis assessment using CartoSound anatomical mapping is feasible during lead extraction. Its role as a complementary surveillance tool to improve procedural outcomes requires extensive validation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.133 | 0.064 |
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".