Using text and acoustic features to diagnose progressive aphasia and its subtypes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents experiments in automatically diagnosing primary progressive aphasia (PPA) and two of its subtypes, semantic dementia (SD) and progressive nonfluent aphasia (PNFA), from the acoustics of recorded narratives and textual analysis of the resultant transcripts. In order to train each of three types of classifier (naive Bayes, support vector machine, random forest), a large set of 81 available features must be reduced in size. Two methods of feature selection are therefore compared – one based on statistical significance and the other based on minimum-redundancy-maximum-relevance. After classifier optimization, PPA (or absence thereof) is correctly diagnosed across 87.4% of conditions, and the two subtypes of PPA are correctly classified 75.6% of the time.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it