Progression of aphasia severity (Johnson et al., 2019)
Bibliographic record
Abstract
Background and Purpose: The severity of aphasic impairment in chronic stroke survivors is typically thought to be stable by 6 months postonset. However, a recent study showed that stroke survivors with aphasia experience language improvement or decline in the chronic phase, years beyond onset. Little is known about why some individuals improve whereas others remain stable or decline. Additionally, no study has tracked changes in aphasia from assessments completed at multiple time points across many years. The current study offers a comprehensive analysis of potential predictive demographic and health information to determine which factors predict dynamic changes in aphasia severity in chronic stroke.Methods: Individuals in the chronic stage of a single-event, left-hemisphere ischemic stroke were identified from an archival database and included for study (N = 39). Participants were included if they had undergone 2 or more standardized language assessments acquired at time points at least 6 months apart, with the 1st assessment at least 6 months postinjury. A linear mixed-effects model was used to determine the impact of treatment and a variety of demographic and health factors on language change.Results: Over time, half of the participants improved (51%), whereas approximately a quarter (26%) decreased, and a quarter (23%) remained stable. A greater number of aphasia treatment hours significantly predicted language improvement (p = .03), whereas older stroke age was associated with long-term decline (p = .04). Two interactions were found to be significant in predicting improvement in individuals with diabetes: Increased exercise and younger age at stroke were significant in predicting outcomes (p < .05).Conclusions: Factors that significantly influence language recovery in chronic aphasia include stroke age and receiving aphasia treatment. For those with diabetes, increased exercise was shown to improve outcomes. Results from this study offer clinicians greater insight into the influence of patient factors on long-term recovery from stroke aphasia while suggesting a potential adjunct to language therapy: exercise. Supplemental Material S1. Individual participant data. Johnson, L., Basilakos, A., Yourganov, G., Cai, B., Bonilha, L., Rorden, C., & Fridriksson, J. (2019). Progression of aphasia severity in the chronic stages of stroke. American Journal of Speech-Language Pathology, 28, 639–649. https://doi.org/10.1044/2018_AJSLP-18-0123
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".