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Record W4394331575 · doi:10.6084/m9.figshare.21774514

Normative data for the Color Trails Test in middle-aged and elderly Quebec-French people

2022· dataset· en· W4394331575 on OpenAlexaffabout
Anne-Sophie Gaudreau, Joël Macoir, Carol Hudon

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNormativeTest (biology)GerontologyPsychologyGeographyMedicinePolitical scienceLawBiology

Abstract

fetched live from OpenAlex

Despite the widespread use of the Color Trails Test (CTT) in clinical and research settings, information regarding the impact of sociodemographic variables on test performance in Quebec-French adults and elderly people is non-existent. This study aimed to establish French-Quebec normative data for error scores and completion time on all test trials (CTT1 and CTT2) taking into account the impact of age, education, and sex on test performance. The sample consisted of 169 community-dwelling and healthy Quebec-French individuals aged between 50 and 90 years and having between 6 and 21 years of formal education. Regression analyses indicated that age was associated with completion time on CTT1 and CTT2. Spearman correlations also revealed that age was positively associated with error scores (CTT1 errors, CTT2 number errors, CTT2 near-misses) and index interference. Education was marginally associated with CTT1 but was not associated with CTT2 completion time or interference index. Education was only associated with the number of errors in the CTT2. Finally, sex was not associated with any variables. Equations to calculate <i>Z</i> scores and percentiles are presented. Norms for the CTT will ease the interpretation of executive functioning in Quebec-French adults and the elderly and favor accurate discrimination between normal and pathological cognitive states.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.893
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.8950.001

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.

Opus teacher head0.159
GPT teacher head0.352
Teacher spread0.193 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes2
Has abstractyes

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