Prevalence and Reliability of Phonological, Surface, and Mixed Profiles in Dyslexia: A Review of Studies Conducted in Languages Varying in Orthographic Depth
Bibliographic record
Abstract
The influence of orthographic transparency on the prevalence of dyslexia subtypes was examined in a review of multiple-case studies conducted in languages differing in orthographic depth (English, French, and Spanish). Cross-language differences are found in the proportion of dissociated profiles as a function of the dependent variables (speed or accuracy), the classification method (classical vs. regression-based methods), and the control sample (chronological age vs. reading level controls). The classical method results in a majority of mixed profiles, whereas the regression-based method results in a majority of dissociated profiles. However, the regression-based method appears to result in less reliable subtypes within and between languages. Finally, reading-level comparisons revealed that the phonological subtype reflects a deviant developmental trajectory across all languages, whereas the surface subtype corresponds to a delayed developmental trajectory. The results also indicate that reading speed should be considered to correctly classify dyslexics into subtypes, at least in transparent orthographies.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".