Bibliographic record
Abstract
Since the discovery of X-rays, medical imaging has played a major role in the guidance of surgical procedures. Recent advances in computer technology have only accelerated the rapid development of this field. As interventions become significantly less invasive, the use of pre-operative and intra-operative images to guide surgery has assumed increasing importance. Image-guided techniques have been employed for many years to plan and guide neurosurgical procedures. Amongst the most challenging areas of neurosurgery is the accurate targeting of nuclei within the deep brain for the treatment of Parkinson's and other motor system diseases. Unfortunately, standard CT and MR imaging does not permit the anatomical delineation of the targets, and so additional information, for example atlases and electrophysiological data, must also be employed. Both these forms of data can be mapped, using non rigid image registration techniques, to a standard representation of a brain acquired from MRI. The electrophysiology database can also evolve over time with the incorporation of data acquired from multiple patients operated in the past. Information of this nature can then be incorporated within the patient image, and serve as an invaluable tool in predicting to the surgeon the likely area of the target. This approach can significantly reduce the trauma associated with the insertion of multiple unnecessary electrodes to refine the target location, and speed up the procedure.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".