L’apprentissage sans erreur : un principe efficace d’intervention dans la maladie d’Alzheimer et dans l’aphasie primaire progressive
Bibliographic record
Abstract
Alzheimer’s disease (AD) and primary progressive aphasia (PPA) are age-related neurodegenerative diseases characterized by a slowly progressive cognitive decline that significantly impacts functional autonomy. Cognitive interventions remain one of the most useful management perspectives to help patients compensate for their cognitive and functional deficits in everyday life. Errorless learning represents a set of principles and methods aimed at eliminating or minimizing errors in a learning context, which was initially applied to patients with an amnesic syndrome. In this article, we examine the effectiveness of this learning principle in the context of AD and PPA. Based on current data from the literature, errorless learning appears to be useful in (re)learning new information or procedural skills in AD and APP, such as relearning names or certain independent activities of daily living. In addition, the benefits of errorless learning are maintained at follow-up. There are, however, discrepancies in the results between studies which could reflect differences in the learning methods employed and in the parameters of the interventions. In conclusion, such interventions should primarily target learning that is useful for patients and that allows them to preserve their autonomy longer and improve their quality of life.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".