Identifying functional cognitive disorder: a proposed diagnostic risk model
Bibliographic record
Abstract
BACKGROUND: Functional cognitive disorders (FCD) are an important differential diagnosis of neurodegenerative disease. The utility of suggested diagnostic features has not been prospectively explored in "real world" clinical populations. This study aimed to identify positive clinical markers of FCD. METHODS: Adults with cognitive complaints but not dementia were recruited from memory, neurology, and neuropsychiatry clinics. Participants underwent structured interview, Mini International Neuropsychiatric Interview, Montreal Cognitive Assessment, Luria 3-step, interlocking fingers, digit span and Medical Symptom Validity Test, Patient Health Questionnaire 15, Hospital Anxiety and Depression Scale, Multifactorial Memory Questionnaire, and Pittsburgh Sleep Quality Inventory. Potential diagnostic variables were tested against expert consensus diagnosis using logistic regression. RESULTS: FCD were identified in 31/49 participants. Participants with FCD were younger, spoke for longer when prompted "Tell me about the problems you've been having," and had more anxiety and depression symptoms and psychiatric diagnoses than those without FCD. There were no significant differences in sex, education, or cognitive scores. Younger age and longer spoken response predicted FCD diagnosis in a model which explained 74% of diagnostic variability and had an area under the curve (AUC) of 94%. CONCLUSIONS: A detailed description of cognitive failure is a sensitive and specific positive feature of FCD, demonstrating internal inconsistency between experienced and observed function. Cognitive and performance validity tests appear less helpful in FCD diagnosis. People with FCD are not "worried well" but often perform poorly on tests, and have more anxiety, depression, and physical symptoms than people with other cognitive disorders. Identifying diagnostic profiles is an important step toward parity of esteem for FCDs, as differential diagnoses of neurodegenerative disease and an independent target for clinical trials.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".