Additional file 1 of Genetic heterogeneity on sleep disorders in Parkinson’s disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Additional file 1: Table S1. Studies excluded because of non-pathogenic genes of PD. Table S2. Study Evaluation according to Newcastle-Ottawa Quality Assessment Scale. Table S3. General characteristics of included studies. Table S4. Primary outcomes of included studies. Table S5. Results of meta-regression in patients with gene variants. Fig. S1. Severity of EDS in PD patients with and without GBA variants. Fig. S2. Risk (a) and severity (b) of EDS in PD patients with and without LRRK2 variants, severity of EDS in PD patients with and without LRRK2 G2019S variants (c). Fig. S3. PDSS score (a) and risk of RLS (b) in PD patients with and without LRRK2 variants. Fig. S4. Funnel plots for the risk of RBD in PD patients with LRRK2 variants. Fig. S5. Risk (a) and severity (b) of RBD in PD patients with PRKN variants. Fig. S6. Risk (a) and severity (b) of EDS in PD patients with PRKN variants. Fig. S7. Risk of RLS in PD patients with and without PRKN variants. Fig. S8. Risk (a) and severity (b) of RBD in asymptomatic carriers with GBA variant and HCs. Fig. S9. Risk (a) and severity (b) of RBD in asymptomatic carriers with LRRK2 G2019S and HCs. Fig. S10. Risk (a) and severity (b) of EDS in asymptomatic carriers with LRRK2 G2019S and HCs.
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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 | 0.005 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.732 | 0.024 |
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".