NI-52 * TOWARDS IMPROVED INTEGRATION OF ADVANCED IMAGING TECHNIQUES INTO THE NEUROSURGICAL OPERATING SUITE: A CANADIAN EXPERIENCE
Bibliographic record
Abstract
BACKGROUND: Based on a prior needs-based questionnaire performed by our group, qualitative evidence suggests that neurosurgeons feel that image processing techniques can lead to safer and more effective operations. A wealth of image processing applications has been developed over the past several decades for the facilitation of improved surgical planning and image guidance including image registration, segmentation (of tumor, peri-tumoral edema and relevant structures), and diffusion tractography. Some of these tools have been successfully integrated into commercial surgical planning and neuronavigation systems, either as part of core functionality or as add-on packages. The Canadian healthcare system presents unique challenges to the integration of advanced imaging techniques into the clinical workflow. With limited resources, hospital administrators invest in medical devices that provide a balance of improvements in patient care while also proving to be cost effective. While neuronavigation itself has become standard of care, investment in additional commercially-available advanced imaging applications can be difficult to justify. METHODS: In collaboration with researchers at Robarts Research Institute, we have developed a framework for improved integration of advanced imaging protocols into the clinical workflow. RESULTS: We have devised a platform for the practical application and testing of image processing software for neurosurgical purposes. Our findings have been summarized in a schematic diagram outlining the workflow for the translation of image processing technologies to the operating room. CONCLUSIONS: We have developed a clinical framework that allows for use of essential commercial surgical planning and neuronavigation workstations, as well as concurrently taking advantage of university-level medical imaging expertise. This collaborative framework provides a backbone for knowledge translation from research to clinical realms and acts as a first step toward more standardized surgical planning and intra-operative neuronavigation.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".