Bibliographic record
Abstract
If the brain is the most complicated material object in the known universe, the mind is its most wondrous product (1). Precisely how the brain generates mental events is an unsolved mystery. The field of neuropsychiatry concerns itself with psychopathology caused by structural brain disease, brain electrical malfunction, and extrinsic toxic metabolic disturbances. As such, neuropsychiatry is ideally placed to study brain–behaviour correlations wherein the underlying biological disturbance can be identified and often localized to specific brain regions. The advent of increasingly sophisticated structural and functional imaging, permitting visualization of the brain in vivo, has given new life to this undertaking. In this issue, Dr Anthony Feinstein reviews the neuropsychiatry of multiple sclerosis, which causes both localized and diffuse brain injury (2). The full spectrum of psychiatric syndromes occurs in association with this disease; however, studies to date have failed to make convincing correlations between specific lesion locations and the syndromes of depression, mania, psychosis, and cognitive-intellectual disturbance. Progress in the neurodegenerative diseases has now moved beyond histopathology to molecular pathology. The data suggest that neurodegenerative diseases can be classified according to a signature molecular abnormality that allows them to be b roadly d ivided into tauopathies and a lphasynucleinopathies, based upon the accumulation of abnormal intracellular tau or alpha-synuclein proteins. These abnormal protein accumulations result in, or are associated with, cellular death. Dr Craig Hou, Dr Danielle Carlin, and Dr Bruce Miller take this approach to classification in their review of the non-Alzheimer’s disease dementias (3). While the molecular pathology helps us better understand the causes of cell death, brain–behaviour correlations are still best understood in terms of injury to an aggregate of neurons with similar functions operating within large-scale networks (4). The non-Alzheimer’s disease dementias provide rich models for these brain–behaviour correlations. Frontotemporal dementia is associated with disinhibition, apathy, and repetitive behaviours; primary progressive aphasia and semantic dementia are associated with speech and language disturbances. In all 3 conditions, cognitive-intellectual failure is a late-occurring event. Dr Feinstein and Dr Hou and colleagues, respectively, review the available and emerging treatments in the neuropsychiatry of multiple sclerosis and the non-Alzheimer’s disease dementias. Treatment of the non-Alzheimer’s disease dementias is in its infancy, with no treatments that stop progressive neurodegeneration. Treatment of the associated behavioural disturbances in frontotemporal dementia is currently based upon augmenting serotonergic, rather than cholinergic, function. In multiple sclerosis, the psychiatric syndromes of depression, mania, and psychosis are treated according to general psychiatric principles. These syndromes respond to the same therapies that are effective in primary psychiatric disorders. These therapies remain the state-of-the-art treatment approach, not only for the psychiatric syndromes associated
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".