A systematic review of Phonological Components Analysis therapy studies for aphasia
Bibliographic record
Abstract
Among the wide range of anomia treatments for persons with aphasia (PWA), Phonological Components Analysis (PCA) is a well-known alternative. A systematic review of PCA efficacy studies for PWA was conducted to extract treatment-related and participant-related characteristics, to synthesise immediate and long-term outcomes and to assess the methodological quality of PCA studies (PROSPERO pre-registration CRD42024552047). Experimental studies on adults with post-stroke aphasia focusing on the efficacy of PCA published in English were included. Studies combining PCA with other treatment approaches, involving people with neurodegenerative disorders, without efficacy measures, or in dissertations, theses, and conference papers were excluded. The EBSCOhost platform and citations of the original PCA paper were last searched in November 2024. In total, thirteen studies were selected involving 89 PWA. Participants were at least 6 months post-stroke, and 75 % of them presented with Broca's or anomic aphasia. The quality of PCA efficacy studies was relatively high according to the Single Case Experimental Design scale (mean 8.6 ± 1.0, range 7-10). Picture naming improved to reach at least a small effect size in 74 % of PWA (58/85) for trained items immediately after PCA and in 55 % of PWA (38/71) in the maintenance phase. Generalisation to untrained items occurred in 37 % of participants (22/59). Overall, PCA led to positive outcomes in the majority of PWA, which were often item-specific. As experimental designs were highly heterogeneous, further research is needed to better understand the optimal target population for PCA, the ideal dosage distribution, the key ingredients driving the improvement, and their neural correlates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".