<i>Taking Control of Your Functional Cognitive Symptoms: Workbook</i> —A Novel Intervention
Bibliographic record
Abstract
OBJECTIVE: Functional cognitive disorder (FCD) is a subtype of functional neurological disorder (FND). FCD may include various cognitive symptoms, precipitants, and comorbid conditions (such as other FNDs, concussion, fatigue, or fibromyalgia). However, no widely available behavioral health interventions exist for FCD. The authors developed a therapist-guided and patient-led treatment for veterans and civilians with FCD. METHODS: A well-known evidence-based treatment for functional seizures (an FCD-adjacent condition often with cognitive symptoms) was adapted to fit hypothesized mechanisms of FCD. The process used a health research format following the guidance for reporting intervention development studies. Key processes included determining the broad intervention framework, obtaining detailed FCD-specific content based on expert consensus, collecting evidence, developing theory, conducting target population-centered approaches, considering specialty subgroups, and gathering feedback from veteran and civilian stakeholders. RESULTS: Initial feasibility, tolerability, and utility were assessed with two target-population stakeholders with FCD (one civilian patient and one veteran patient), with both reporting a Patient Global Impression of Change scale rating of 1 (indicating that their condition had very much improved). CONCLUSIONS: This promising new multimodality behavioral health intervention can be considered to be in stage 1 (i.e., intervention generation, refinement, modification, adaptation, and pilot testing). Further pilot testing is being conducted and will need to be followed by traditional efficacy testing (in stage 2).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".