Diagnostic validity of biomarkers in Parkinson’s Disease: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To identify biomarkers for Parkinson's disease, cerebrospinal fluid, blood, saliva, and urine. METHOD: The studies were collected from the Cochrane, LILACS, PubMed, SCOPUS, WEB OF SCIENCE, OpenGrey, ProQuest and Google Scholar databases starting from May 3, 2016 and updated on March 20, 2017. Twenty-two studies were evaluated, by the Quality Assessment Tool for Diagnostic Accuracy Studies and Review Manager 5.3. RESULTS: Evidence shows that serum antibodies can be used as highly specific and accurate biomarkers for the diagnosis of Parkinson's disease at the outset. Biomarkers in the cerebrospinal fluid are related to increased motor severity, postural instability, gait abnormality, and cognitive impairment. CONCLUSION: Serum and cerebrospinal antibodies can be used as diagnostic biomarkers at the onset of the disease.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.024 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".