Surgery of the motor thalamus: Problems with the present nomenclatures
Bibliographic record
Abstract
The literature on thalamic surgery is difficult to read because different nomenclatures are in use. Neurosurgeons mostly use the stereotactic atlas of Schaltenbrand with Hassler's nomenclature of the thalamus. Neuroanatomists use different nomenclatures for the primate thalamus. The cytoarchitectonic definition of nuclei is difficult in the motor thalamus, and it would be best to define the nuclei based on their subcortical afferents. However, tracing studies are not available in humans. Thus, human thalamic nomenclature is based entirely on cytoarchitectonic subdivisions and transfer of knowledge by analogy from monkey to man. Problems arise when trying to transfer the detailed knowledge from monkey to the human brain. By doing so, different authors have come to different conclusions concerning the subcortical afferents of Hassler's motor nuclei, which inevitably leads to confusion when attempting neurophysiological interpretations of the surgical data. The present review draws attention to the discrepancies and open questions in the literature. There is a need to better define the limits of the sensory and cerebellar afferent receiving thalamic nuclei as well as those of the cerebellar and pallidal afferent receiving territories in humans.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".