Aprosodia Subsequent to Right Hemisphere Brain Damage: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Objective: To identify which aspects of prosody are negatively affected subsequent to right hemisphere brain damage (RHD) and to evaluate the methodological quality of the constituent studies. Method: Twenty-one electronic databases were searched to identify articles from 1970 to February 2020 by entering keywords. Eligibility criteria for articles included a focus on adults with acquired RHD, prosody as the primary research topic, and publication in a peer-reviewed journal. A quality appraisal was conducted using a rubric adapted from Downs and Black (1998). Results: Of the 113 articles appraised as eligible and appropriate for inclusion, 71 articles were selected to undergo data extraction for both meta-analyses of population effect size estimates and qualitative synthesis. Across all domains of prosody, the effect estimate was g = 2.51 [95% CI (1.94, 3.09), t = 8.66, p < 0.0001], based on 129 contrasts between RHD and non-brain-damaged healthy controls (NBD), indicating a significant random effects model. This effect size was driven by findings in emotional prosody, g = 2.48 [95% CI (1.76, 3.20), t = 6.88, p < 0.0001]. Overall, studies of higher quality ( r pb = 0.18, p < 0.001) and higher sample size/contrast ratio ( r pb = 0.25, p < 0.001) were more likely to report significant differences between RHD and NBD participants. Conclusions: The results confirm consistent evidence for emotional prosody deficits in the RHD population. Inconsistent evidence was observed across linguistic prosody domains and pervasive methodological issues were identified across studies, regardless of their prosody focus. These findings highlight the need for more rigorous and sufficiently high-powered designs to examine prosody subsequent to RHD, particularly within the linguistic prosody domain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".