Expected performance of Quebec-French older adults on the <i>Batterie Rapide de Dénomination</i> (BARD)
Bibliographic record
Abstract
Difficulties retrieving words during conversations, called anomia, are frequent in the late preclinical stage of Alzheimer’s disease, in mild cognitive impairment, and in major neurocognitive disorders. Picture-naming tests, used to assess anomia, are too lengthy and are unsuitable for medical or nursing practices. The main objective of this study was to confirm the usefulness of the Batterie Rapide de Dénomination (BARD - Battery of Rapid Denomination), an electronically-administered picture-naming test comprising 10 pictures for which perfect naming scores were obtained in French, English and Flemish participants. In this study, the BARD was applied on 207 healthy, French-speaking participants from Quebec, aged 50 years and more, wherein the results demonstrated the ease of naming the 10 pictures of the test. However, their performance was not always flawless. Seven out of the 207 participants of produced one error on one specific picture, which led us to excluding this item from the BARD. Thus, this study established the utility of the BARD for clinical settings of French-Quebec populations. This screening test is ideally suited for bedside assessment in acute care settings, stroke units and medical environments providing primary and secondary care. Its use has the potential for improving referrals to more specialized resources.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".