A qualitative analysis of patients’ day-to-day living with functional cognitive disorder (FCD)
Bibliographic record
Abstract
PURPOSE: Functional cognitive disorder (FCD) is an increasingly recognised condition which causes significant disability and distress. While it is known to be associated with depression, anxiety and reduced functioning, like other functional neurological disorders it may be marred by stigma. To date, little is known about the lived experience of those with FCD. MATERIALS & METHODS: As part of a randomised controlled feasibility trial of online group Acceptance and Commitment Therapy (ACT) for FCD we conducted in-depth qualitative interviews and utilised a thematic analysis approach to explore the challenges of living with FCD; how individuals cope with the symptoms; and their experiences of healthcare services. RESULTS: We recruited six women and three men, with a median age of 54. They described living with FCD which included experiences of loss of identity, altered role, feelings of confusion, frustration and low self-esteem, stigma, social isolation, and dismissal by healthcare services. Nevertheless, suffers strove to find strategies to ameliorate their symptoms. CONCLUSIONS: FCD is a common and disabling condition which like other functional neurological symptoms leads to altered sense of self and unsatisfactory interactions with others, including healthcare professionals. The diagnosis should be made on positive grounds, clearly explained, and potential therapies investigated.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".