Diagnostic accuracy and predictors of alpha-synuclein accumulation in the gastrointestinal tract of Parkinson’s disease
Bibliographic record
Abstract
The only characteristic of alpha-synuclein (AS) accumulation in the gastrointestinal (GI) tract of Parkinson's disease (PD) found in pathological studies is the "rostrocaudal gradient," which describes the more frequent presence of AS accumulation in the upper GI tract than in the lower GI tract. This study aimed to determine the diagnostic accuracy and identify predictors of AS accumulation in the GI tract of PD patients. The frequency of AS accumulation in the GI tract was compared between PD patients (N = 97) who underwent radical GI surgery for cancer and individually matched controls (N = 94). We evaluated AS accumulation in the neural structures using phosphorylated AS immunohistochemistry. A multivariable logistic regression analysis was conducted to determine the predictors of AS accumulation in the GI tract of PD patients. The frequency of AS accumulation was significantly higher in PD patients (75.3%) than in controls (8.5%, p-value < 0.001). The sensitivity and specificity of the full-layer evaluation were 75.3% and 91.5%, respectively. When the evaluation was confined to the mucosal/submucosal layer, the sensitivity and specificity were 46.9% and 94.7%, respectively. The rostrocaudal gradient of AS accumulation was found in PD patients. The duration from symptom onset to surgery was significantly longer in PD patients with AS accumulation (4.9 ± 4.9 years) than in PD patients without AS accumulation (1.8 ± 4.1 years, p-value = 0.005). Both disease duration and rostrocaudal gradient independently predicted the presence of AS accumulation in the GI tract of PD patients. Our study suggests PD-related AS accumulation in the GI tract follows a temporally increasing but spatially static progression pattern.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".