NEUROSURGERY IN THE REALM OF 10−9, PART 1
Bibliographic record
Abstract
Nanotechnology as a science has evolved from notions and speculation to emerge as a prominent combination of science and engineering that stands to impact innumerable aspects of technology. Medicine in general and neurosurgery in particular will benefit greatly in terms of improved diagnostic and therapeutic capabilities. The recent explosion in nanotechnology products, including diverse applications such as beauty products and medical contrast agents, has been accompanied by an ever increasing volume of literature. Recent articles from our institution provided an historical and scientific background of nanotechnology, with a purposeful focus on nanomedicine. Future applications of nanotechnology to neuroscience and neurosurgery were briefly addressed. The present article is the first of two that will further this discussion by providing specific details of current nanotechnology applications and research related to neuroscience and clinical neurosurgery. This article also provides relevant perspective in scale, history, economics, and toxicology. Topics of specific importance to developments or advances of technologies used by neuroscientists and neurosurgeons are presented. In addition, advances in the field of microelectromechanical systems technology are discussed. Although larger than nanoscale, microelectromechanical systems technologies will play an important role in the future of medicine and neurosurgery. The second article will discuss current nanotechnologies that are being, or will be in the near future, incorporated into the armamentarium of the neurosurgeon. The goal of these articles is to keep the neuroscience community abreast of current developments in nanotechnology, nanomedicine, and, in particular, nanoneurosurgery, and to present possibilities for future applications of nanotechnology. As applications of nanotechnology permeate all forms of scientific and medical research, clinical applications will continue to emerge. Physicians of the present and future must take an active role in shaping the design and research of nanotechnologies to ensure maximal clinical relevance and patient benefit.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".