Correlates of depressive symptoms in individuals attending outpatient stroke clinics
Bibliographic record
Abstract
Background and purpose Depressive symptoms are common post-stroke. We examined stroke deficits and lifestyle factors that are independent predictors for depressive symptomology. Methods A retrospective chart review was performed for patients’ post-stroke who attended outpatient clinics at a hospital in Southwestern Ontario between 1 January 2014 and 30 September 2014. Demographic variables, stroke deficits, secondary stroke risk factors and disability study measures [Patient Health Questionnaire-9 (PHQ-9) and Montreal Cognitive Assessment (MoCA)] were analyzed. Results Of the 221 outpatients who attended the stroke clinics (53% male; mean age = 65.2 ± 14.9 years; mean time post-stroke 14.6 ± 20.1 months), 202 patients were used in the final analysis. About 36% of patients (mean = 5.17 ± 5.96) reported mild to severe depressive symptoms (PHQ-9 ≥ 5). Cognitive impairment (CI), smoking, pain and therapy enrollment (p < 0.01) were significantly associated with depressive symptoms. Patients reporting CI were 4 times more likely to score highly on the PHQ-9 than those who did not report CI (OR = 4.72). While controlling for age, MoCA scores negatively related to depressive symptoms with higher PHQ-9 scores associated with lower MoCA scores (r= −0.39, p < 0.005). Conclusions High levels of depressive symptoms are common in the chronic phase post-stroke and were partially related to cognition, pain, therapy enrollment and lifestyle factors.Implications for RehabilitationStroke patients who report cognitive deficits, pain, tobacco use or being enrolled in therapy may experience increased depressive symptoms.A holistic perspective of disease and lifestyle factors should be considered while assessing risk of depressive symptoms in stroke patients.Patients at risk for depressive symptoms should be monitored at subsequent outpatient visits. Stroke patients who report cognitive deficits, pain, tobacco use or being enrolled in therapy may experience increased depressive symptoms. A holistic perspective of disease and lifestyle factors should be considered while assessing risk of depressive symptoms in stroke patients. Patients at risk for depressive symptoms should be monitored at subsequent outpatient visits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".