Bibliographic record
Abstract
Parkinson's disease is a common movement disorder associated with considerable disability. The clinical syndrome of parkinsonism is based on the presence of core clinical features of rest tremor, bradykinesia, rigidity and impaired postural reflexes or gait. Parkinsonism is most often caused by Parkinson's disease, but can also be caused by other disorders, including cerebrovascular disease, multiple-system atrophy, progressive supranuclear palsy and other disorders. Parkinsonism can be identified by questionnaires and confirmed in person or by videotaped clinical examinations. The identification of presymptomatic cases remains problematic but is motivated by the hope for treatment before symptoms appear. Quantitative approaches to the diagnosis of parkinsonism based on the measurement of the cardinal features are available. Clinical approaches should include identification of features atypical for Parkinson's disease, which exclude the diagnosis, and documentation of a response to dopaminergic medications, which support a diagnosis of Parkinson's disease. Loss of smell and visual dysfunction are found in early patients and may be useful in screening protocols. In addition, behavioral changes, including depressive symptoms, may be detected in presymptomatic cases. Cognitive changes, such as impaired set shifting, have been observed in early Parkinson's disease, but can be seen with other causes of parkinsonism. Neuroimaging techniques, including positron emission tomography or single-photon emission computed tomography, are available to quantify dopaminergic neurons, while magnetic resonance imaging may be helpful in differentiating other forms of parkinsonism from Parkinson's disease. There are numerous approaches available to the identification of parkinsonism and Parkinson's disease. The gold standard remains a clinical diagnosis, confirmed by autopsy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".