CSF β-amyloid <sub>42</sub> and risk of freezing of gait in early Parkinson disease
Bibliographic record
Abstract
<h3>Objective</h3> To determine whether CSF biomarkers can be used as a predictor of freezing of gait (FOG) in Parkinson disease (PD) and to investigate the predictive value of clinical, dopamine transporter (DAT) imaging, and CSF parameters both separately and in combination. <h3>Methods</h3> This study using the PPMI data included 393 patients with newly diagnosed PD without FOG at baseline. We evaluated CSF for β-amyloid 1-42 (Aβ<sub>42</sub>), α-synuclein, total tau, phosphorylated tau<sub>181</sub>, and the calculated ratio of Aβ<sub>42</sub> to total tau at baseline. Demographic and clinical data and DAT imaging results were also investigated. Cox proportional-hazards regression analyses were performed to identify the factors predictive of FOG. From these results, we constructed a predictive model for the development of FOG. <h3>Results</h3> During a median follow-up of 4.0 years, only Aβ<sub>42</sub> among the CSF biomarkers was associated with the development of FOG (hazard ratio 0.997, 95% confidence interval [CI] 0.996–0.999, <i>p</i> = 0.009). Postural instability gait difficulty (PIGD) score, caudate DAT uptake, and, to a lesser extent, male sex, Movement Disorders Society Unified Parkinson9s Disease Rating Scale motor score, and Montreal Cognitive Assessment score were also predictive of FOG. The combined model integrating the PIGD score, caudate DAT uptake, and CSF Aβ<sub>42</sub> achieved a better discriminative ability (area under the curve 0.755, 95% CI 0.700–0.810) than any factor alone. <h3>Conclusion</h3> We found CSF Aβ<sub>42</sub> to be a predictor of FOG in patients with early PD. Furthermore, the development of FOG within 4 years after diagnosis of PD can be predicted with acceptable accuracy with our risk model.
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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 | 0.000 | 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".