The Impact of Poststroke Aphasia on Quality of Life: A Comparative Cross-Sectional Study
Bibliographic record
Abstract
Objectives: The study aimed to analyze the impact of aphasia on quality of life (QoL) in persons with ischemic stroke per radiological severity, compare equally severe but nonaphasic stroke survivors, and analyze the impact of hyperbaric oxygen therapy (HBOT) exposure. Methods: Patients with first-ever middle cerebral artery (MCA) stroke were categorized by radiological severity into high, intermediate, and low Alberta Stroke Program Early CT Score (ASPECTS). The Stroke Aphasia Quality of Life (SAQoL) Scale was used for outcome analysis. Inclusion criteria were age 40-65, 12-16 months after stroke, MCA distribution, first stroke, and ischemic stroke. Exclusion criteria were mixed vessel involvement and concomitant neurological, orthopedic, or psychiatric comorbidities. Results: Among 93 patients with ischemic stroke, 87% presented with intermediate-to-low ASPECTS. According to the SAQoL, locomotion and transfers were the most compromised. QoL was significantly negatively correlated with higher ASPECTS and greater stroke impact in those with aphasia overall (p = 0.001). Those who received HBOT overall were significantly better than those who did not, regardless of group (p = 0.02 and 0.03). Conclusion: The present study shows that the radiological severity of stroke relates to QoL in those with poststroke aphasia. Among those with equal radiological severity, those with aphasia are worse off. Those who receive HBOT have better QoL.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".