dCALQ – a new screening test for the acquired deficits of number processing: Development and normative data
Bibliographic record
Abstract
The clinical management of number processing disorders is often overlooked, partly due to the lack of standardized assessment tools specifically designed for acalculia. For French-speaking clinicians and researchers, there is currently no assessment battery grounded in theoretical models of cognitive psychology for diagnosing acalculia in adults and the elderly. This study had two main objectives: to develop a practical, and standardized French-Quebec screening test battery for the cognitive assessment of acquired number processing disorders, and to provide normative data based on the performance of younger and older adults living in the French Quebec community. The Batterie de détection des troubles acquis du traitement des nombres et du calcul de Québec (dCALQ), developed in Study 1, included 20 subtests to assess number processing in three main domains, namely: Recognition and comprehension of digits and numbers, Digit and number production abilities and, Calculation processes. In Study 2, normative data for the dCALQ were established by analyzing the performance of 260 healthy, community-dwelling, French-speaking adults aged 50 to 90 years, with 6 to 23 years of formal education. The dCALQ is a new clinical, theoretically based screening assessment battery designed to assist clinicians in detecting acquired acalculia associated with various neurological disorders.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".