P.003 Health-related quality of life (HRQOL) for genetically determined leukoencephalopathy patients and their families
Bibliographic record
Abstract
Background: Genetic leukoencephalopathies are a group of neurodegenerative diseases imposing a great burden on patients and families. There is no previous systematic study looking at the impacts of these diseases. Methods: HRQOL was assessed using the Pediatric Quality of Life Inventory (PedsQL) model. A total of 24 patients with genetically determined leukoencephalopathies and their family members completed the PedsQL questionnaires. Detailed clinical assessments were performed at the time the questionnaires were filled. HRQOL results were correlated with the severity of the clinical features and the presence vs. absence of a definitive molecular diagnosis. Results: Preliminary results show lower PedsQL total scores for patients without compared to with a molecular diagnosis. Emotional and physical functioning scores were significantly impaired in patients without a molecular diagnosis. Lower total scores were obtained for patients who presented more severe clinical features such as lost ambulatory functions and dysphagia. Conclusions: Overall, our preliminary results indicate that patients without a molecular diagnosis have an impaired HRQOL and that more severely affected patients have a poorer HRQOL. Further analyses and studies on a larger population of patients in a prospective fashion are required to assess the burden of these diseases and identify potential modifiable factors.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".