Association of Proportional Recovery After Stroke With Health-Related Quality of Life
Bibliographic record
Abstract
Background and Purpose: No data exists on whether proportional recovery (PR) is associated with health-related quality of life (HRQOL) domains. We evaluated whether PR was associated with domain-specific HRQOL scores at 3 months after ischemic stroke. Methods: This prospective cohort study enrolled patients with ischemic stroke between January 2017 and June 2018. Impaired strength was assessed using the Fugl-Meyer Upper Extremity (range, 0–66 points) and Motricity Index (range, 0–100 points) during index hospitalization and 3 months. Both measures are well-validated and reliable in patients with stroke to assesses motor functioning. PR (defined as 70% of difference between initial score and maximum possible recovery) was calculated from the initial measurements. HRQOL was measured using Neuro-QOL domains: upper extremity, depression, and cognition domains. PR was evaluated with HRQOL domains using binomial logistic regression. Results: Final analysis included 84 patients (mean age 67.8±16.4 years; 44% male; 51.2% White). For both Fugl-Meyer Upper Extremity and Motricity Index, the PR threshold was met for 48.8% of patients. Failure to meet Motricity Index PR was only associated with increased odds of HRQOL depression impairment (adjusted odds ratio, 11.8 [95% CI, 1.23–112.7]). Failure to meet Fugl-Meyer Upper Extremity PR threshold was not associated with HRQOL impairment after adjustment. Conclusions: Our findings suggest that reaching the PR threshold provides poor discrimination of HRQOL. Despite not meeting expected PR thresholds, patients can still maintain un-impaired HRQOL, suggesting other factors play a role in preserved HRQOL.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".