MDS Research Criteria for Prodromal Pakrinson's Disease (P5.353)
Bibliographic record
Abstract
Objective: To describe research criteria and probability methodology for the diagnosis of prodromal Parkinson's disease. Background: As efforts to design disease-modifying therapy against Parkinson' disease advance, it is becoming increasingly recognized that earlier recognition and treatment are key to effective treatment. Prodromal Parkinson's disease refers to the stage wherein early symptoms or signs of Parkinson's neurodegeneration are present, but clinical diagnosis based on fully-evolved motor parkinsonism is not yet possible. So far, there is no systematic method to identify patients with prodromal PD. Methods/Results: The criteria estimate the probability that an individual patient has prodromal PD. Probable prodromal PD is defined as >80[percnt] certainty of neurodegeneration being present. Probability is estimated using a Bayesian naive classifier. In this methodology, a prior probability of prodromal disease is delineated based upon age. Then, diagnostic information is added, expressed as likelihood ratios. This diagnostic information combines estimates of background risk (from environmental risk factors and genetic findings) and results of diagnostic marker testing. In order to be included, diagnostic markers had to have prospective evidence documenting ability to predict clinical PD. They include motor and non-motor clinical symptoms, clinical signs, and ancillary diagnostic tests. Once all diagnostic information is collected, likelihood ratios are multiplied by each other to calculate a combined likelihood ratio. From this and the baseline probability, an individual's final probability of prodromal PD is calculated. Conclusions: The new MDS prodromal PD criteria represent a first step in the formal delineation of early stages of PD. Their methodology is new; no previous neurologic diagnostic criteria have used data to calculate actual risk for an individual patient. These criteria provide a means to identify early PD patients for disease-modifying neuroprotective trials.
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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 | 0.001 |
| 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".