Relationship of clinical and quality of life trajectories following the onset of seizures: Findings from the UK MESS Study
Bibliographic record
Abstract
PURPOSE: We defined a series of clinical trajectories represented among adult patients with new-onset seizures across a 4-year follow-up period; and linked these clinical trajectories to the quality of life (QOL) profiles and trajectories of those experiencing them. We examined both between- and within-group differences. METHODS: Analyses were based on 253 individuals completing QOL questionnaires at baseline and 2 and 4 years subsequently. Based on patient self-report, we defined five "clinical trajectory" groups: individuals experiencing a single seizure only; individuals entering early remission; individuals experiencing late remission; individuals initially becoming seizure-free but subsequently relapsing; individuals with seizures persisting throughout follow-up. QOL profiles at each time point were compared using a validated QOL battery, NEWQOL. KEY FINDINGS: Even at baseline, there were significant between-group differences, with patients experiencing a single seizure only reporting the best QOL profile and those with seizures subsequently persisting across all time points reporting the worst. By 2 years, the QOL profiles of individuals experiencing early remission were similar to those of single seizure patients, as were those for late remission and relapse patients. SIGNIFICANCE: A consistent pattern was seen, with "single seizure" individuals doing best and individuals with persistent seizures doing worst. Of particular concern is that even at baseline, individuals whose seizures persisted were doing poorly for QOL, suggesting the possibility that underlying neurobiologic mechanisms were operating. In contrast, our findings support previous reports of only short-lived and small QOL decrements for individuals experiencing a single or few seizures.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".