Post-stroke depression, obstructive sleep apnea, and cognitive impairment: Rationale for, and barriers to, routine screening
Bibliographic record
Abstract
Stroke can cause neurological impairment ranging from mild to severe, but the impact of stroke extends beyond the initial brain injury to include a complex interplay of devastating comorbidities including: post-stroke depression, obstructive sleep apnea, and cognitive impairment ("DOC"). We reviewed the frequency, impact, and treatment options for each DOC condition. We then used the Ottawa Model of Research Use to examine gaps in care, understand the barriers to knowledge translation, identification, and addressing these important post-stroke comorbidities. Each of the DOC conditions is common and result in poorer recovery, greater functional impairment, increased stroke recurrence and mortality, even after accounting for traditional vascular risk factors. Despite the strong relationships between DOC comorbidities and these negative outcomes as well as recommendations for screening based on best practice recommendations from several countries, they are frequently not assessed. Barriers related to the nature of the screening tools (e.g., time consuming in high-volume clinics), practice environment (e.g., lack of human resources or space), as well as potential adopters (e.g., equipoise surrounding the benefits of treatment for these conditions) pose challenges to routine screening implementation. Simple, feasible approaches to routine screening coupled with appropriate, evidence-based treatment protocols are required to better identify and manage depression, obstructive sleep apnea, and cognitive impairment symptoms in stroke prevention clinic patients to reduce the impact of these important post-stroke comorbidities. These tools may in turn facilitate large-scale randomized controlled treatment trials of interventions for DOC conditions that may help to improve cardiovascular outcomes after stroke or TIA.
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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 | 0.057 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| 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".