An Increased Risk of Stroke among Panic Disorder Patients: A 3-Year Follow-up Study
Bibliographic record
Abstract
OBJECTIVE: To explore whether panic disorder (PD) increases the risk for stroke, using a nationwide, population-based dataset. METHODS: Our study used data from Taiwan's National Health Insurance Research Database. The study cohort included patients who received ambulatory psychiatric care for PD between 2002 and 2003, inclusive (n = 3891). We selected our comparison cohort by randomly recruiting enrollees (n = 19 455) matched with the study group by sex and age. Each patient was tracked for 3 years, from their index ambulatory care visit until the end of 2006, to identify whether or not a patient had a stroke during the follow-up period. Cox proportional hazard regressions were performed as a means of computing the 3-year survival rate, adjusting for potential confounding factors. RESULTS: Among the total sample, 2029 patients (8.7%) experienced a stroke during the 3-year follow-up period, including 647 from the study cohort (16.6% of the PD patients) and 1382 (7.1%) from the comparison cohort. After adjusting for the patients' sex, age, monthly income, level of urbanization, and comorbid medical disorders, the hazard of stroke occurring during the 3-year follow-up period was 2.37 (P < 0.001) times greater for patients with PD than for patients in the comparison cohort. In further analyses, stratified by medical diseases and age, the significant risk of PD on subsequent stroke persisted. CONCLUSIONS: We conclude that PD is an independent risk factor for stroke. For patients with PD, aggressive treatment of PD may be considered as part of stroke prevention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".