Associations of cognitive test performance with self-reported mental health, cognition, and quality of life in adults with functional seizures: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective: People with functional seizures (FS) have frequent and disabling cognitive dysfunction and mental health symptoms, with low quality of life. However, interrelationships among these constructs are poorly understood. In this meta-analysis, we examined associations between objective (i.e. performance-based) cognitive testing and self-reported (i) mental health, (ii) cognition, and (iii) quality of life in FS. Method: We searched MEDLINE, Embase, PsycINFO, and Web of Science, with the final search on June 10, 2024. Inclusion criteria were studies documenting relationships between objective cognitive test scores and self-reported (i.e. subjective) mental health, cognition, and/or quality of life in adults with FS. Exclusion criteria were mixed FS/epilepsy samples. A modified Newcastle-Ottawa Scale evaluated risk of bias. This project is registered as CRD42023392385 in PROSPERO. Results: Initially, 4,054 unique reports were identified, with the final sample including 24 articles of 1,173 people with FS. Mean age was 35.9 (SD = 3.9), mean education was 12.6 (SD = 1.3), and proportion of women was 73.9%. Risk of bias was moderate, due in part to inconsistent reporting of confounding demographic variables. Significant relationships were found between global objective cognition and global self-reported mental health (k = 21, Z = −0.23 [0.04], 95% CI = −0.30, −0.16), depression (k = 11, Z = −0.13 [0.05], 95% CI = −0.21, −0.04), cognition (k = 5, Z = −0.16 [0.05], 95% CI = −0.26, −0.06), and quality of life (k = 5, Z = −0.17 [0.05], 95% CI = −0.24, −0.10). Exploratory analyses showed associations between select cognitive and mental health constructs. Conclusions: Objective cognition is reliably associated with self-reported mental health, cognition, and quality of life in people with FS. Scientific and clinical implications are discussed.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".