Bibliographic record
Abstract
The cerebellum has intricate connections either directly or indirectly with most levels of the brain. While many of these are functionally relevant, prominent neuropathologic changes that reflect the underlying anatomy arise from injury to a few major pathways. Identifying these pathologic sequelae can help to identify the cells involved in the underlying disease. The cerebellum has two distinct components: the aesthetically pleasing cortical dendritic folia and the deep nuclei. The uniform and monotonous cerebellar cortex contains molecular, Purkinje, and granular layers. Several deep nuclei are the main efferent projections from the cerebellum. Key neuron types relevant to cerebellar pathology include the cortical excitatory granular input and inhibitory Purkinje output neurons, the deep nuclear excitatory projection neurons, as well as key afferent nuclei (eg, inferior olivary, pontine, and sensory neurons) and efferent targets (eg, red nucleus, thalamus). Pathologic processes that affect one or more of these neuron groups lead to stereotypic anatomical changes, which in turn signal the existence of those processes. This article first reviews key aspects of human cerebellar anatomy and histology, then discusses some key connections, and finally presents a selection of cases from humans that illustrate these interconnections.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".