Functional Cognitive Disorder: Diagnostic Challenges and Future Directions
Bibliographic record
Abstract
Functional cognitive disorder describes patients with persistent, troublesome subjective cognitive complaints that are inconsistent with a recognized disease process, and where significant discrepancies are found between subjective and objectively observed cognitive functioning. The etiology is heterogeneous and potentially related to underlying psychological factors. Making a diagnosis of functional cognitive disorder can be challenging and there is the potential for misdiagnosis of early-stage neurodegeneration. We compared neuropsychological findings in three groups: functional cognitive disorder (FCD), mild cognitive impairment (MCI), and healthy controls. Participants were recruited from the ReMemBr Group Clinic, North Bristol NHS Trust, and via Join Dementia Research. Both the FCD and MCI groups showed elevated prospective and retrospective memory symptom scores. Performance on the Montreal cognitive assessment was equivalent in the FCD and MCI groups, both being impaired compared with the controls. The FCD group was younger than those with MCI. We discuss challenges and controversies in the diagnosis of functional cognitive disorder, alongside illustrative cases and proposals for areas of research priority.
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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 | 0.055 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.020 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.015 | 0.014 |
| 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".