Diagnostic Challenges Revealed from a Neuropsychiatry Movement Disorders Clinic
Bibliographic record
Abstract
BACKGROUND: Abnormal movements are frequently associated with psychiatric disorders. Optimized management and diagnosis of these movements depends on correct labeling. However, there is evidence of reduced accuracy in the labeling of these movements, which could result in sub-optimal care. OBJECTIVE: To determine the consensus inter-rater reliability between a movement disorders neurologist and physicians referring from the community for phenomenology and diagnoses of individuals with co-existing psychiatric conditions and movement disorders. METHOD: Charts of all consecutive patients seen in a combined Movement Disorders and Neuropsychiatry Clinic between 2001-2009 were reviewed retrospectively. Consensus estimates and kappa values for inter-rater reliability were determined for phenomenology and diagnostic terms for the respective referring source and movement disorders neurologist for each patient. RESULTS: A total of 106 charts were reviewed (62 men and 44 women). Agreement for phenomenology terms ranged from 0% (psychogenic) to 73% (tremor). Only 3 terms had kappa values that met or exceeded criteria for moderate inter-rater reliability. Agreement for diagnosis terms was highest for tardive dyskinesia (83%), drug induced tremor (33%), and drug induced parkinsonism (20%). In 18 of the 22 charts (82%), a diagnosis was made of drug induced movement disorder (DIMD) by the referring physician. In contrast, a diagnosis of DIMD was made in only 54 of 106 charts (51%) after the patients were assessed in the clinic. CONCLUSIONS: A movement disorders specialist frequently disagreed with referring physicians' identification of patient phenomenology and diagnosis. This suggests that clinicians would benefit from educational resources to assist in characterizing abnormal movements.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".