Predictors of Survival in a Huntington's Disease Population from Southern Italy
Bibliographic record
Abstract
BACKGROUND: The primary aim of the present study was to determine the survival rates and identify predictors of disease duration in a cohort of Huntington's disease (HD) patients from Southern Italy. METHODS: All medical records of HD patients followed between 1977 and 2008 at the Department of Neurological Sciences of Federico II University in Naples were retrospectively reviewed and 135 patients were enrolled in the analysis. At the time of data collection, 41 patients were deceased (19 males and 22 females) with a mean ± SD age at death of 56.6 ± 14.9 years (range 18-83). RESULTS: The median survival time was 20 years (95% CI: 18.3-21.7). Cox regression analysis showed that the number of CAG in the expanded allele (HR 1.09 for 1 point triplet increase, p=0.002) and age of onset (HR 1.05 for 1 point year increase, p=0.002) were independent and significant predictors of lower survival rates. CONCLUSIONS: We believe that these findings are important for a better understanding of the natural history of the disease and may be relevant in designing future therapeutic trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".