Anti-inflammatory treatment and risk for depression
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 antidepressant 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".