Abstract WP427: Health-related Quality of Life Measures in Familial Cerebral Cavernous Malformation Patients
Bibliographic record
Abstract
Background: Patient-reported quality of life (QoL) using standardized tools have been proposed as outcome measures in clinical trials. The NeuroQoL scale has been evaluated in patients with common neurological disorders (e.g., ischemic stroke, epilepsy, ALS, and Parkinson’s), but has not been applied to familial cerebral cavernous malformation (FCCM). FCCM is typically characterized by multiple brain lesions that can cause clinical symptoms (hemorrhages, seizures, headaches, neurological deficits) and affect QoL. The purpose of this study was to summarize NeuroQoL domain scores in FCCM patients. Methods: NeuroQoL short forms covering 12 QoL domains (Figure) were completed by 50 FCCM adults enrolled in the Brain Vascular Malformation Consortium CCM Project. Raw scores summing responses in each domain were converted to T-scores, which are standardized to general (8 domains) or clinical (4 domains) reference populations with mean of 50 and standard deviation of 10. One-sample t-tests were used to determine whether mean T-scores were significantly different from 50 (p<0.05). Results: We observed significant differences between FCCM and the reference populations on several domains (Figure). Compared to the general reference population, FCCM patients were more likely to have higher positive affect (56, p<0.001) and less depression (55, p<0.001), but lower social satisfaction (48, p=0.032). Compared to the clinical reference population, FCCM patients reported significantly less stigma (61, p<0.001) and less fatigue (54, p=0.018). Conclusion: FCCM patients differed from the reference populations on several NeuroQoL domains, including positive affect, depression, stigma, fatigue and social satisfaction. Further studies are needed to understand the effect of clinical symptoms on NeuroQoL domains in FCCM.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".