Persistent depression is a predictor of quality of life in stroke survivors
Bibliographic record
Abstract
BACKGROUND: Whether the time course of post-stroke depression (PSD) can be used to predict the quality of life (QoL) of patients with late-stage stroke remains unclear, this study investigated whether persistent depression at 1 year after stroke predicts QoL at 5 years following stroke. METHODS: We analyzed the demographic and clinical data of patients with stroke in 56 hospitals across China that participated in the Prospective Cohort Study on the Incidence and Outcome of Patients with PSD in China Study. Follow-up assessments were performed at the following time points after stroke: in person, 2 weeks, 3 months, 6 months, and 1 year; by telephone, 5 years. National Institutes of Health Stroke Scale (NIHSS) score on admission, recurrence, disability, depression, QoL, and chronic complications were recorded. Depression was diagnosed using the Diagnostic and Statistical Manual of Mental Disorders. QoL was measured using short form-12 (SF-12). Multivariable ordinal logistic regression analysis was used to identify factors that independently affected the physical component summary (PCS) and mental component summary (MCS) scores of the SF-12. RESULTS: Of the 801 patients evaluated in this study, 80 had persistent depression. The multivariable regression analysis of data obtained at 5 years showed that persistent depression at 1 year (odds ratio [OR]: 0.48; 95% confidence interval [CI]: 0.29-0.81) and disability at 5 years (OR: 0.34; 95% CI: 0.23-0.49) were associated with poor MCS scores at 5 years. Old age, a high NIHSS score on admission, disability at 5 years, and stroke recurrence within 5 years were associated with poor PCS scores at the 5-year follow-up. CONCLUSIONS: Persistent depression at the 1-year follow-up could predict poor MCS scores at the 5-year follow-up. The development of interventional strategies targeting post-stroke patients with persistent depression is warranted.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".