Anti-inflammatory treatment and risk for depression after first-time stroke in a cohort of 147 487 Danish patients
Bibliographic record
Abstract
Background: Depression is a common complication after stroke, and inflammation may be a pathophysiological mechanism. This study examines whether anti-inflammatory treatment with acetylsalicylic acid (ASA), nonsteroid anti-inflammatory drugs (NSAIDs) or statins influence the risk of depression after stroke. Methods: A register-based cohort including all patients admitted to hospital with a first-time stroke from Jan. 1, 2001, through Dec. 31, 2011, and a nonstroke population with a similar age and sex distribution was followed for depression until Dec. 31, 2014. Depression was defined as having a hospital contact with depression or having filled prescriptions for anti-depressant medication. The associations between redeemed prescriptions of ASA, NSAIDs or statins with early- (≤ 1 year after stroke or study entry) and late-onset (> 1 year after stroke or study entry) depression were analyzed using Cox proportional hazard regression. Results: We identified 147 487 patients with first-time stroke and 160 235 individuals without stroke for inclusion in our study. Redeemed prescriptions of ASA, NSAIDs or statins after stroke decreased the risk for early-onset depression, especially in patients with ischemic or severe stroke. Patients who received a combination of anti-inflammatory treatments had the lowest risk for early-onset depression. On the other hand, use of ASA or NSAIDs 1 year after stroke increased the risk for late-onset depression, whereas statin use was associated with a tendency toward a decreased risk. Limitations: The study used prescription of antidepressant medication as a proxy measure for depression and did not include anti-inflammatory drugs bought over the counter. Conclusion: Anti-inflammatory treatment is associated with a lower risk for depression shortly after stroke but a higher risk for late depression. This suggests that inflammation contributes differently to the development of depression after stroke depending on the time of onset.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".