The efficacy of confrontational naming treatments for aphasia: a meta-analysis
Bibliographic record
Abstract
Background Previous systematic reviews and meta-analyses have only mentioned a few confrontational naming treatment methods. There is no recent literature on the effect of different treatment methods and on comparing their efficacy with each other.Objective The purpose of this study was to: a) collect treatment methods for addressing confrontational naming deficits in patients with aphasia; b) examine the number of studies on treatment methods; c) evaluate the level of evidence for these treatment methods; d) determine the efficacy of the explored treatment methods; and e) compare various treatment methods.Methods We searched Scopus, ISI Web of Sciences, PubMed, Cochrane Library, ProQuest, Clinical Key, Science Direct, and Springer electronic databases for articles published from January 2009 to March 2023. A meta-analysis was conducted using comprehensive meta-analysis (CMA2) software. A funnel plot was applied to examine the influence of outliers. Furthermore, Cochran’s Q test and the I-square (I2) index were utilized to assess the homogeneity of effect sizes in the included studies.Results Of the 1808 articles identified, 66 were included in the final synthesis (1496 participants). Among the studies reviewed, 62.69% were found to have high effect sizes, 19.40% had medium effect sizes, and 17.91% showed an effect size of less than 0.3, indicating low efficacy. The syntactic cueing method, errorless learning, and action observation methods had high effect sizes (E.S = 0.93, E.S = 0.89, E.S = 0.88, respectively). The personalized cueing method had low effect sizes (E.S = 0.22).Conclusions This systematic review extracted and compared various treatment methods for naming deficits in individuals with aphasia. Evidence has shown that syntactic cueing, errorless learning, and action observation methods have the highest effect size and are, therefore, the most effective. Additionally, the SFA and gestural methods are effective interventions, considering the abundance of studies, high level of evidence, and substantial effect size.
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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 | 0.026 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.053 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".