Body composition in Spinocerebellar ataxia type 3 and 10 patients: Comparative study with control group
Bibliographic record
Abstract
Background: Spinocerebellar ataxias (SCAs) are a group of neurodegenerative genetic diseases characterized by movement disorders that can affect nutritional status and body composition. This study sought to assess body composition in SCA3 and SCA10 patients. Methods: Anthropometric assessments and bioelectric impedance analysis were performed in 46 SCA3 and SCA10 patients and 76 controls of both genders. Results: Of the patients, 69.6% had SCA3 and 58.7% were women. SCA3 patients had significantly lower percentages of body fat (%BF) than controls (15.0 ± 6.1 vs. 20.6 ± 7.1; p=0.014) and (22.4 ± 6.9 vs. 30.1 ± 6.0; p<0.001), respectively. Among the women, there was a statistically significant difference in %BF between SCA3 and SCA10 patients (22.4 ± 6.9 vs. 32.4 ± 4.9; p<0.001). Male and female SCA3 patients had significantly lower fat-free mass (FFM) than controls [50.6 kg (46.9–54.7) vs. 58.6 kg (52.6–63.9); p=0.001] and [38.2 kg (35.1–42.6) vs. 42.8 kg (39.7–46.1); p=0.004], respectively. Male SCA10 patients also had lower FFM than controls [51.2 kg (47.1–55.4) vs. (52.6–63.9); p=0.008]. Female SCA10 patients had significantly higher FFM than controls and SCA3 patients [45.0 kg (43.3–45.6) vs. 42.8 kg (39.7–46.1); p=0.004] and [45.0 kg (43.3–45.6) vs. 38.2 kg (35.1–42.6); p=0.004], respectively. There was moderate correlation (−0.42) between disease duration and muscle mass (MM), and weak (−0.38) between SARA (Scale for the Assessment and Rating of Ataxia) and MM in SCA3. In SCA10, there was no significant correlation between these variables. Conclusion: Female SCA3 patients had more body composition changes than female SCA10 patients, mainly in relation to FFM. SCA3 and SCA10 patients need nutritional follow-up to minimize body compartment changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".