P.006 Engineering neurosurgery: role of inter-disciplinary collaboration in development of a remote controlled stereotactic system
Bibliographic record
Abstract
Background: Well-crafted engineering solutions have overcome technical challenges faced by surgeons. We present a collaborative effort to develop an innovative solution aimed at saving time and subsequently operating room costs in procedures utilizing a traditional stereotactic system. Methods: We met with our University’s local engineering team to collaborate a solution over a much-appreciated intra-operative technology gap with respect to mechanical adjustment of a stereotactic frame’s co-ordinates. AUTO-CAD software simulated our design, which was materialized with a 3D printer using PLA (polyactic acid). Results: We present a novel stereotactic system where co-ordinates can be digitally entered remotely to localize a point in 3D space. As such, this automated stereotactic frame decreases operative time when compared to manually adjusting a traditional stereotactic system such as the Leksell system. In addition our remote controlled stereotactic system helps minimize human-factor risks and allows one the option to modify stereotactic system co-ordinates from a non-sterile field. Conclusions: Marriage between Engineering and Neurosurgery can improve clinical outcomes for patients suffering from neurological diseases. We provide a grass roots organization’s attempt at overcoming an operative need by designing a remote controlled stereotactic system.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".