Prevalence of Undiagnosed Risk Factors in Patients with First-Ever Ischemic Stroke Treated at MUHC: A Retrospective Analysis
Bibliographic record
Abstract
Background: Ischemic stroke is a leading cause of morbidity and mortality worldwide. Despite established prevention strategies, many patients present with previously undiagnosed vascular risk factors (URFs) at the time of their first-ever ischemic stroke, suggesting missed opportunities for early detection. In Canada, particularly in Quebec, access to primary care is inconsistent, and a substantial proportion of the population lacks attachment to a family doctor (FD). Objective: This study aimed to determine the prevalence of URFs among patients with first-ever ischemic stroke and to evaluate the relationship between URFs, geographic region, and access to primary care in Quebec, Canada. We hypothesized that patients without an FD would have a higher prevalence of URFs. Methods: We conducted a retrospective chart review of 610 patients admitted with first-ever ischemic stroke to the McGill University Health Center (MUHC) between 2014 and 2017. Data collected included demographics; known and undiagnosed stroke risk factors such as hypertension (HTN), diabetes mellitus (DM), hyperlipidemia (HLD), and atrial fibrillation (AF); FD status; and geographic location based on postal code. Results: Among the 610 patients, 136 (22.3%) had at least one URF. The most common URF was HLD (14.3%), followed by HTN (6.2%), AF (1.6%), and DM (0.1%). Of 609 patients with available data, 146 (23.97%) lacked an FD. Patients without an FD were significantly more likely to have undiagnosed HTN (7.6% vs. 2.1%, p = 0.008). No significant differences were observed for the other URFs. Geographic variation was noted in both URF prevalence and FD access, but regional differences were not statistically significant. Conclusions: Our findings support the hypothesis that a lack of an FD is associated with a higher prevalence of undiagnosed HTN in ischemic stroke patients. Targeted screening and improved access to primary care, particularly in underserved regions, may help to reduce the burden of preventable stroke by facilitating the earlier identification and management of modifiable risk factors.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".