Letter: Microsurgical Clipping of an Anterior Communicating Artery Aneurysm Using a Novel Robotic Visualization Tool in Lieu of the Binocular Operating Microscope: Operative Video
Bibliographic record
Abstract
To the Editor: We would like to congratulate the authors on their recent publication of an operative video in Operative Neurosurgery on the microsurgical clipping of an anterior communication artery aneurysm using a robotic visualization tool.1 The authors describe their clinical experience with a robotic exoscope referred to as the BrightMatter TM Servo System (Synaptive Medical, Toronto, Ontario).1 Note that the utilization of this system can offer an alternative to the conventional surgical microscope. However, the authors do remark that to the best of their knowledge, “the use of this device for the microsurgical clipping of an intracranial aneurysm has never been described in the literature.” We would like to bring to their attention a publication (abstract) in PubMed published in August of 2016, describing the use of this technology in repairing 6 intracranial aneurysms over the previous year.2 We suspect the authors likely may have just inadvertently not found the previous publication (August 2016) reference describing the application of this technology in aneurysm surgery in their search. In addition, a subsequent larger series of broader applications in intracranial surgery, including aneurysms, was published in May of 2017.3 We would be appreciative if the authors, in any way, could acknowledge the previous work. Again, we would like to congratulate the authors for their work in documenting the application of this technology. Disclosures Dr Kassam has the following disclosures: (1) Synaptive Medical (consultant), (2) KLS Martin (consultant), and (3) Medtronic Medical (advisory board). The other authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".