Clinical markers for identifying cholinergic deficits in Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Cholinergic projection systems degeneration is associated with dopamine nonresponsive features of Parkinson's disease (PD). Cholinergic deficits are variable in nondemented PD. Identification of cholinergic deficits in PD may help with selection of suitable patients for targeted cholinergic drug treatment in PD. The objective of this retrospective multivariate predictor analysis study was to identify clinical markers indicative of cholinergic deficits in PD patients, as assessed by acetylcholinesterase ([(11) C]PMP) positron emission tomography. METHODS: One hundred thirty-seven PD patients (34 female) participated; median modified Hoehn and Yahr score was 2.5 (range, 1-4), average age 65.6 ± 7.4 years, and average duration of motor disease symptoms of 6.0 ± 4.2 years. Subjects were dichotomized as "normocholinergic" or "hypocholinergic" based on a 5(th) percentile cutoff from normal for the basal forebrain-cortical and pedunculopontine nucleus-thalamic cholinergic projection systems. Previously identified clinical indices of cholinergic denervation were used for statistical prediction of cholinergic deficits. Logistic regression determined which risk factors predicted cholinergic deficits. Sensitivity, specificity, and accuracy were determined for the (combinations of) significant predictor variables. RESULTS: Forty-nine (35.8%) hypocholinergic PD subjects were identified. The combination of rapid eye movement (REM) sleep behavior disorder (RBD) symptoms and fall history showed highest diagnostic accuracy (81.1%) for predicting combined thalamic and cortical cholinergic deficits. A combined assessment of 8.5 m walk time and lower score on the Montreal cognitive assessment scale provided diagnostic accuracy of 80.7% for predicting isolated cortical cholinergic denervation. CONCLUSION: Assessment of clinical indices of cholinergic denervation may be useful for identifying suitable subjects for trials of targeted cholinergic drug treatments in PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".