Quebec French Version of the Hayling Sentence Completion Test: Error Scoring Guidelines, Normative Data for Adults and the Elderly and Validation Study in Mild Cognitive Impairment and Alzheimer’s Disease
Bibliographic record
Abstract
INTRODUCTION: Deficits in inhibition have been associated with various clinical conditions, including neurodegenerative diseases. The Hayling Sentence Completion Test (HSCT) is an assessment tool commonly used in clinical settings to measure verbal initiation and prepotent verbal response inhibition. Although it is used by numerous clinical and research groups in Quebec, normative data for the HSCT are not yet available for French-Quebec speakers. OBJECTIVES: The aims of this study were to provide error scoring guidelines and normative data in the adult population of French Quebec for the HSCT-QC (Study 1) and to determine its known-group discriminant validity (Study 2). RESULTS: The results of Study 1, based on a sample of 214 healthy individuals aged 50 to 89, indicated that age significantly affected test performance, while educational level and sex did not. As no transformations were able to normalize the score distribution, percentile ranks for HSCT-QC performance were calculated solely based on age. Results from Study 2 demonstrated that the HSCT-QC effectively distinguishes the performance of healthy participants from those with mild cognitive impairment or Alzheimer's disease. CONCLUSION: Norms and psychometric data for the HSCT-QC will be highly beneficial for assessing inhibitory control in French-speaking adults in Quebec, Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".