Lexa: A tool for detecting dyslexia through auditory processing
Bibliographic record
Abstract
Dyslexia can be broken down into two categories, surface and phonological dyslexia. Surface dyslexia is the problem of reading the word as a whole, whereas phonological dyslexia is the problem of sounding out the parts of a word. Researchers are mostly interested in phonological dyslexia, as it is more severe. Dyslexia is mostly detected once a child is able to read and displays signs of reading difficulties. Using phonological markers to detect dyslexia before a child is able to read would have substantive benefits for being able to intervene early in their reading development. The goal of this ongoing work is to develop a software application that can be used by parents before their child is able to read to predict whether a child is at risk for developing dyslexia. Data on children performance of phonological tasks was acquired from a 2009 UK study conducted by Dr. Goswami. The data was analyzed to determine which phonological processing tasks were the best predictors of dyslexia. The best predictors were the tasks of oddity and rise time, and a prototype application was built using these tasks.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".