Maintenance and Generalization of Lexical Items in Primary Progressive Aphasia: Reflections From the Roundtable Discussion at the 2021 Clinical Aphasiology Conference
Bibliographic record
Abstract
PURPOSE: Our capacity to engage in society and maintain meaningful relationships is dependent on intact communication skills. They are compromised in a neurodegenerative language disorder termed primary progressive aphasia (PPA). Behavioral interventions for PPA are sparse and often limited to impairment-based approaches or communication skills training, although various functional interventions have been also described. The slow but relentless language decline does not naturally support maintenance and/or generalization of treatment gains, which should be the ultimate goal of any therapy. However, in some cases and under certain conditions, maintenance and generalization may be accomplished. While each type of intervention has much to offer to the PPA population, the clinical and research realms can benefit from a collective professional discussion on aspects of intervention conducive to maintenance and/or generalization of treatment gains in PPA. Such a discussion took place at the 2021 Clinical Aphasiology Conference during two roundtable sessions. The aims of the sessions were to review the premises of successful treatment approaches in PPA and to discuss factors fostering or inhibiting maintenance and generalization in PPA. CONCLUSIONS: Current literature delivers, albeit in small doses, encouraging evidence for clinicians providing language intervention to patients with PPA. Although PPA is a progressive disorder, both the immediate treatment effects and, in many cases, evidence of maintenance and generalization demonstrate that improvements may be long lasting and transferrable. Several factors may enhance maintenance and generalization effects, including repeated practice, working with multiple exemplars of treatment items, booster sessions, group programs with built-in individual sessions, spared semantics, and personal relevance, to name a few. With this evidence in hand, we need to become more diligent about measuring and reporting clinical outcomes and delivering interventions that support maintenance and generalization of therapeutic gains beyond the clinician's office. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.19836370.
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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 | 0.039 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.031 | 0.041 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".