First Neurological Evaluation and Course of Parkinson's Disease (P1.003)
Bibliographic record
Abstract
Objective: To determine the significance of clinical motor findings at first neurological evaluation to the prognosis in PD. Background: Non-motor features may precede motor symptoms of Parkinson’s disease (PD). The diagnosis is however based on a combination of bradykinesia (akinesia), rigidity and tremor. When the diagnosis of PD is made by a neurologist the patient wants pertinent information including the expected outcome, which has major significance to the individual/family. It is well known that patients with akinetic-rigid phenotype have the worst and tremor dominant cases the best outcome. However, an individual patient’s phenotype can change over time. Methods: Patients evaluated at Movement Disorders Clinic Saskatchewan (MDCS) are offered autopsy at no cost. We have previously reported on the course of different lifelong motor phenotypes in 166 autopsied PD cases. For this study, we identified the severity of motor features at initial MDCS assessment to determine its value in predicting the prognostic phenotypes in that group. Those with motor symptoms of 15 years or longer duration at first visit were excluded from this study. Results: 156 (98 M) autopsy confirmed PD - 39 (25[percnt]) akinetic-rigid, 106 (68[percnt]) mixed and 11 (7[percnt]) tremor dominant cases had satisfactory clinical baseline data on bradykinesia, rigidity and tremor. Of those cases 70 (45[percnt]) were not on any anti-parkinsonian drugs at initial evaluation. The baseline motor features in 156 cases positively predicted the phenotype in 85[percnt] of all - 90[percnt] of akinetic-rigid, 88[percnt] of the mixed, but in only 55[percnt] of the tremor dominant cases. In the 70 untreated cases, the lifelong phenotyping could be predicted at baseline in 91[percnt] patients. Conclusions: Our data show that initial neurological assessment can provide a valuable guide to the course of disease in most PD cases. Such cases can be used for future research to identify reliable prognostic biomarkers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".