Dysphagia is a risk factor of malnutrition in X-linked Dystonia-Parkinsonism
Bibliographic record
Abstract
• EAT-10 total scores were negatively correlated with BMI twelve months later in XDP. • EAT-10 total scores predicted BMI twelve months later in XDP. • EAT-10 total scores ≥ 4 predicted malnutrition twelve months later in XDP. Malnutrition is a leading cause of death for persons living with X-linked dystonia-parkinsonism (XDP), a degenerative disease endemic to the Philippines. Difficulty swallowing has been linked to malnutrition in other populations; however, knowledge of this relationship is limited in XDP. As such, the purpose of this study was to determine the association between dysphagia and malnutrition in this population. 21 individuals with XDP, 26 controls, and 18 genetic carriers were included in the final data analysis. Spearman’s rank order correlation coefficient was used to determine an association between baseline EAT-10 total scores and 12-month malnutrition status, and multiple linear regression to evaluate the predictive ability of the EAT-10. A baseline EAT-10 score cut-off point predicting 12-month malnutrition status was estimated. For the XDP group, the baseline EAT-10 total scores had a significant negative correlation ( r = -0.68, p < 0.001) with and was a significant predictor ( p = 0.001) of 12-month BMI. A baseline EAT-10 total score of ≥ 4 predicted malnutrition twelve months after administration (sensitivity = 0.93; specificity = 1; AUC = 0.95). Dysphagia, as measured using the EAT-10, was associated with BMI in the XDP population. Additionally, an EAT-10 total score ≥ 4 could predict malnutrition in twelve months after test administration. With these findings, healthcare providers could identify patients with XDP at high risk for malnutrition earlier and provide intervention sooner.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".