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Record W4407354779 · doi:10.1080/23279095.2025.2464065

dCALQ – a new screening test for the acquired deficits of number processing: Development and normative data

2025· article· en· W4407354779 on OpenAlexaffabout
Joël Macoir, Carol Hudon, Anne Lafay

Bibliographic record

VenueApplied Neuropsychology Adult · 2025
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNormativeTest (biology)PsychologyComputer scienceBiologyEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.354
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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