IJCARS—IPCAI 2022 special issue: 13th international conference on information processing in computer-assisted interventions 2022—part 1
Bibliographic record
Abstract
We are delighted to present this special issue of International Journal of Computer Assisted Radiology and Surgery (IJCARS) as part 1 of the proceedings of the 13th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI) 2022.IPCAI will be held in conjunction with the Computer Assisted Radiology and Surgery (CARS) congress.The contributions contained in this special issue will be presented on June 7th and 8th during the hybrid event in Tokyo.Since 2010, IPCAI has been a premier international and interdisciplinary forum that brings together clinicians, computer scientists, engineers and other researchers in a unique setting.From the beginning, the aim of the meeting was to foster active engagement of the attendees by providing session formats that allow for in-depth discussions of the topics presented.Putting strong focus on translational research at the interface between computational and engineering sciences and clinical and interventional practice distinguishes IPCAI from other conferences in the field.Key topics include surgical data science, interventional imaging, interventional robotics, tracking and navigation, surgical planning and simulation, augmented reality and advanced visualization as well as surgical skill analysis and workflow.Throughout the last years, two types of contributions to IPCAI emerged: (1) Regular Papers as full, journal-style contributions in which authors can present their research in depth, and (2) Long Abstracts that are intended to showcase novel ideas, software platforms, or recent breakthroughs, including those with preliminary but limited experimental 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.043 | 0.038 |
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".