Additional file 1 of Comprehensive cross-sectional and longitudinal comparisons of plasma glial fibrillary acidic protein and neurofilament light across FTD spectrum disorders
Bibliographic record
Abstract
Supplementary Material 1: Table S1: Summary of relatedness when considering each phenotype group separately. Table S2: Summary of relatedness in each phenotype group in comparison to controls. Table S3: Summary of relatedness in each phenotype group in comparison to the presymptomatic group. Table S4: Breakdown of baseline and longitudinal biomarker measures and rates of their change in phenotype groups. Table S5: Correlations between baseline GFAP and baseline NfL. Table S6: Associations of baseline biomarkers with age, sex, symptom duration, and BMI in phenotype groups – unadjusted analysis. Table S7: Associations of baseline biomarkers with age, sex, symptom duration, and BMI in phenotype groups – adjusted analysis. Table S8: Comparisons of baseline biomarkers between controls or presymptomatic mutation carriers and phenotype groups – unadjusted analysis. Table S9: Comparisons of baseline biomarkers between controls or presymptomatic mutation carriers and phenotype groups – adjusted analysis. Table S10: Baseline biomarker comparisons between asymptomatic groups and symptomatic groups stratified by symptom duration – unadjusted analysis. Table S11: Baseline biomarker comparisons between asymptomatic groups and symptomatic groups stratified by symptom duration – adjusted analysis. Table S12: Comparisons of baseline GFAP and NfL concentration between disease groups. Table S13: AUC values from models including both biomarkers when comparing asymptomatic groups to phenotype groups. Table S14: Comparisons of baseline biomarkers between asymptomatic groups and mildly impaired phenotype groups – unadjusted analysis. Table S15: Comparisons of baseline biomarkers between asymptomatic groups and mildly impaired phenotype groups – adjusted analysis. Table S16: Comparison of baseline biomarkers or their rates of change between controls and phenoconverters or non-converters. Table S17: AUC values from models including both biomarkers when comparing controls to phenoconverters or non-converters. Table S18: Comparison of baseline biomarkers or their rates of change by mutation status in asymptomatic groups. Table S19: Comparison of rate of change in GFAP and NfL concentrations between controls and phenotype groups. Table S20: Characteristics of disease severity indicators according to disease group. Table S21: Associations of baseline GFAP with baseline disease indicators in FTD syndromes and MBCI – unadjusted analysis. Table S22: Associations of baseline GFAP with baseline disease indicators in FTD syndromes and MBCI – adjusted analysis. Table S23: Associations of baseline NfL with baseline disease indicators in FTD syndromes and MBCI – unadjusted analysis. Table S24: Associations of baseline NfL with baseline disease indicators in FTD syndromes and MBCI – adjusted analysis. Table S25: Associations of baseline biomarkers with rates of disease indicator change in all FTD syndromes combined. Table S26: Associations of baseline GFAP and NfL with survival after symptom onset. Table S27: Comparisons of baseline biomarkers or their ratio by mutation status in all FTD syndromes combined. Table S28: Comparisons of baseline biomarkers or their ratio among mutation groups in all FTD syndromes combined. Table S29: Differences in baseline biomarkers or their ratio between FTLD-tau or FTLD-TDP pathology in FTD syndromes
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.821 | 0.101 |
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".