The neurological examination adapted for neuropsychiatry
Bibliographic record
Abstract
The neuropsychiatric examination includes standard neurological and cognitive examination techniques with several additional observations and tasks designed to capture abnormalities common among patients with neuropsychiatric disorders or neurocognitive complaints. Although useful as a screening tool, a single standardized rating scale such as the Mini Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA) is insufficient to establish a neuropsychiatric diagnosis. Extra attention is paid to findings commonly seen in the setting of psychiatric disorders, dementias, movement disorders, or dysfunction of cortical or subcortical structures. Dysmorphic features, dermatologic findings, neurodevelopmental signs, signs of embellishment, and expanded neurocognitive testing are included. The neuropsychiatric clinician utilizes the techniques described in this article to adapt the examination to each patient's situation, choosing the most appropriate techniques to supplement the basic neurological and psychiatric examinations in support of diagnostic hypotheses being considered. The added examination techniques facilitate diagnosis of neurocognitive disorders and enable neuropsychiatric formulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".