The Role of the Multimedia Platforms and of Virtual Simulators in Teaching Vascular Laparoscopic Surgery
Bibliographic record
Abstract
Université Pierre & Marie CURIE & CHU, Paris. This issue of the Journal contains the abstracts of the International Endovascular and Laparoscopic Congress held in Quebec City, Canada, May 20-22, 2004. The reader will find abstracts treating the latest and most significant issues in the field of endovascular surgery, centering particularly on endovascular grafting. The raison d'être of the Congress is to promote the development of laparoscopic vascular surgery. Experts in both fields of minimally invasive vascular surgery presented the latest devices, techniques, and results. The free paper session was of high quality, even taking into account the recent recognition of laparoscopy in vascular surgery. The next Congress, scheduled for May 2006, already promises exciting developments, one of them being the ability to perform an aortic or iliac anastomosis with an automatic device. These devices will be tested clinically during 2005. They are already available for evaluation during laparoscopic vascular hands-on courses (www.vascularlaparoscopy.org). I hope that you will find that these abstracts provide novel information that is helpful in your day-to-day practice. Yves-Marie Dion IELC President
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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 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".