A Digital Diagnosis Tool for Children with Dyslexia against Similar Criteria DSM-5
Bibliographic record
Abstract
Background: we stand on the fact that what makes diagnosing dyslexia difficult is that students with dyslexia are of very normal or even extraordinary intelligence. It is important to prepare a tool that enables the initial diagnosis of this disorder, and in the light of its results, a report is formulated on the degree of injury of the person being tested. Aims: The aim is to develop a reliable and validated tool for children with dyslexia. Methods: A convenience sampling method was used to select children aged 4-8 years as the study population. Inclusion criteria were (1) age between 4 and 8 years; (2) be able to speak; and (3) Keep attending kindergarten and school. Children's parents and teachers were given full information about the study, and all signed an informed written consent form. Ninety children were included in this study. Word spelling Test, Letter rapid automatized naming Test, Recognition of a first sound test, Deletion of a first sound test, Blending test, and Segmentation test was developed and used. Findings: The content validity of all tests was measured. The correlation coefficients between each subscale and the total scale ranged from 0.54 to 0.58. Conclusion: This tool is expected to assist teachers in identifying, accurately assessing, and formulating a learning program that fits the abilities and needs of the children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".