A Longitudinal View of Apathy and Its Impact After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Stroke survivors are often described as apathetic. Because apathy may be a barrier to participation in promising therapies, more needs to be learned about apathy symptoms after stroke. The specific objective was to estimate the extent to which apathy changes with time over the first year after stroke and the impact of apathy on recovery. METHODS: The Apathy Assessed cohort was formed from stroke survivors participating in a longitudinal study of health-related quality of life after stroke. A family caregiver completed an apathy questionnaire by telephone at 1, 3, 6, and 12 months after stroke (n=408). Group-based trajectory modeling and ordinal regression were used to identify distinctive groups of individuals with similar trajectories of apathy over the first year after stroke and predictors of apathy trajectory. RESULTS: Both 3- and 5-group trajectory models fit the data. We used the 5-group model because of the potential to further explore the apathy construct. The largest group (50%) had low apathy and 33% had minor apathy that remained stable throughout the first year after stroke. A small proportion (3%) of the study sample had high apathy that remained high. Two other groups of almost equal size (7%) showed worsening and improving apathy. Poor cognitive status, low functional status, and high comorbidity predicted higher apathy. High apathy had a significant negative effect on physical function, participation, health perception, and physical health over the first 12 months after stroke. CONCLUSION: Some degree of apathy was prevalent and persistent after stroke and was predicted by older age, poor cognitive status, and low functional status after stroke. Even a minor level of apathy had an important and statistically significant impact on stroke outcomes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".