[P1–382]: MULTI‐CLASS DIFFERENTIAL DIAGNOSIS AMONG ALZHEIMER's, PARKINSON's, CORTICOBASAL SYNDROME AND PROGRESSIVE SUPRANUCLEAR PALSY
Bibliographic record
Abstract
Some sulcal regions show expansion with tight high convexity and others contraction.Data from regions with AUROCS in excess of 0.7 were used to build an LDA model with an AUROC of 0.94 (Figure 3).Conclusions: An entirely automated method can be used to predict classification of tight high convexity.By combining both contracted and expanded CSF spaces, the LDA classifier captures many of the patients with DESH, an important radiological feature of iNPH, and is consistent with the hypothesis that tight high convexity is neither atrophy nor inflammation but a shifting of tissue.Figure 3.The ROC curves for the LDA model combining the best regions and the best single expanded and constricted regions.The AUROC for the LDA model is approximately 0.94 indicating that the model has excellent accuracy for tight high convexity classification.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".