Diagnostic implications of the double deficit model for young adolescents with dyslexia
Bibliographic record
Abstract
Considerable support exists for both the phonological core deficit and the naming speed deficit models of dyslexia. The double deficit model proposed that many students with dyslexia might also be impaired in both underlying processes. Employing either performance thresholds (i.e., scores below the 16th or 25th percentile) or k-means clustering as classification methods, the current study investigated whether 154 young adolescents with dyslexia could be categorized into subtypes according to the presence or absence of phonological deficits alone, naming speed deficits alone, or a combination of the two and whether group composition changed depending on classification method. Results support the existence of both single and double deficit groups and confirm that those with both deficits are the most severely impaired across multiple measures. Contrary to previous research, most adolescents were classified as either naming speed only (about a third of the group) or double deficit when defining impairment using performance thresholds to classify groups. This may suggest that although early phonological deficits are amenable to remediation, identification of language symbols fails to become automatized in most individuals with dyslexia and may require more targeted intervention. Classification differences reported in the literature may depend on age and methods employed for 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".