Additional file 1 of Dorsal hyperintensity and iron deposition patterns in the substantia nigra of Parkinson’s disease, idiopathic REM sleep behavior disorder, and Parkinson-plus syndromes at 7T MRI: a prospective diagnostic study
Bibliographic record
Abstract
Additional file 1. eMethods. Table S1 Demographic and clinical characteristics in the pooled dataset. Table S2 Inter-observer reliability of the seven DNH assessment methods in the pooled dataset. Table S3 Visual assessment of the DNH in the development cohort at echo 1-echo 4. Table S4 Visual assessment of the DNH in the pooled dataset at echo 1-echo 4. Table S5 Visual assessment of the DNH in early-stage PD subtypes in the pooled dataset at echo 2. Table S6 Predominant side of DNH impairment and motor symptoms. Table S7 Diagnostic and differential diagnostic performances of the seven DNH assessment methods in the development cohort. Table S8 Diagnostic and differential diagnostic performances of the seven DNH assessment methods in the pooled dataset. Table S9 Validation of diagnostic and differential diagnostic performances of the DNH abnormality. Table S10 Subgroup analyses of diagnostic and differential diagnostic performances of the seven DNH assessment methods in age- and education-matched participants aged over 60. Table S11 Follow-up evaluations of iRBD patients. Fig. S1 Flowchart of participant inclusion. Fig. S2 3D gradient-echo T2* images (echo 1-echo 4) for a representative case from each patient and healthy control groups. Fig. S3 ROC curves for the three DNH rating scales at echo 2 in the pooled dataset. Fig. S4 Receiver operating characteristic curves for the optimal DNH rating scale in the development cohort, its performance in the validation cohort, and its reassessment in the pooled dataset. Analysis S1 Comparison of participant characteristics. Analysis S2 Comparison of diagnostic and differential diagnostic performance between T2* echoes. Analysis S3 Correlation analysis between clinical characteristics and DNH scores in PD and MSA-C patients. Analysis S4 Comparison of clinical characteristics in MSA-P and PSP patients regarding the detectability of the DNH.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.720 | 0.062 |
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".