Novel reading index for identifying disordered reading skill development: A preliminary study
Bibliographic record
Abstract
Children with ADHD are at high risk of developing a Reading Disability (RD), although the reasons remain unclear. ADHD-associated impairments, including processing speed, can complicate clinical evaluation for a co-occurring RD diagnosis. We propose a novel metric to (a) assess reading development and (b) provide an alternative method to classifying readers that may aid investigations for etiologies of RD in ADHD. Specifically, as both phonological decoding and word recognition skills are important precursors of reading fluency, we propose a new quantitative method comparing these skills after accounting for variations in perception, motor response, or processing speeds. Forty boys (14 control, 15 ADHD, 11 ADHD/ + RD) completed a lexical decision task testing decoding and another assessing word recognition. Response time data was modeled using a Drift Diffusion approach to estimate the underlying reading skills. Using these reading skill estimates, we calculated a novel Reading Tendency Index and classified participants into three reading groups (Decoders, Balanced Readers, and Sight Readers). The reading and cognitive performance of these groups were consistent with theoretical predictions and subsequently provided external validity for the novel Reading Tendency Index classification. Our findings demonstrate a potential classification tool for readers based on individual's developed, reading tendencies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".