A longitudinal study of narrative discourse in post-stroke aphasia
Bibliographic record
Abstract
Background Previous findings have demonstrated the importance of discourse analysis in post-stroke aphasia, as it allows for in-depth examination of language impairment and represents key components of functional communication. However, little is known about the recovery of discourse over time.Aims The main aim of this study is to measure the longitudinal changes in descriptive discourse production from the acute to chronic stages of post-stroke aphasia recovery. The secondary aim is to explore the association between discourse measures and overall language impairment severity measures at different testing points.Methods & Procedure Seventeen French-Canadian speakers with various types and severities of aphasia following a first left middle cerebral artery stroke participated in this study. They underwent three language assessments (acute: 0 to 72 hours; subacute: 7 to 14 days; chronic: 6 to 12 months post-onset). The picture description from the Western Aphasia Battery was analyzed at three time points. Changes in terms of thematic informativeness and microstructural variables were analyzed.Outcomes & Results Regarding the micro-structural variables, the mean length of utterances (MLU) and the number of words per minute showed significant positive changes between the acute and chronic phases. For the thematic informativeness measures, the number of thematic units (TUs), the number of thematic units per minute (TUs/min) and the number of thematic units per utterance (TUs/utt) increased significantly between the acute and chronic phases. Positive correlations between TUs and MLU in the acute phase and a general language impairment severity measure in the acute and chronic phases suggest a relationship between these measures and global language performance suggesting the potential predictive value of these variables in the acute phase.Conclusions & Implications These findings support the use of thematic units in descriptive discourse analysis during an acute clinical examination of language as they require minimal additional time to score and track changes in post-stroke aphasia recovery. They capture long-term changes in discourse abilities and appear related to overall language measures in both the acute and chronic stages of recovery. The interpretation of the changes in MLU and the number of words per minute is less straightforward, as improvements in these measures carry different interpretations depending on the type of aphasia. Nonetheless, further studies are required to investigate test-retest reliability and the effect of therapy on the changes observed over time when using thematic units to document change in discourse.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".