Abstract TMP39: Patterns of Discharge Antidepressant Therapy Use After Acute Ischemic Stroke: Insights From the Prosper Study
Bibliographic record
Abstract
Background: Antidepressant (AD) therapy has been shown to improve post-stroke depressive symptoms, yet few data are available on characteristics associated with treatment at the time of discharge after ischemic stroke. Methods: PROSPER is a PCORI-funded study designed by researchers and stroke survivors to evaluate the effectiveness of therapies post-stroke. We used information from ischemic stroke patients discharged from April 2014 - December 2014 in the American Heart Association’s Get With The Guidelines (GWTG)-Stroke registry who were linked to Centers for Medicare and Medicaid Services (CMS) data to evaluate antidepressant medication use following hospitalization for ischemic stroke. Results: Of 29,177 eligible patients from 1,023 hospitals, n=7,593 (26.0%) were prescribed AD at hospital discharge. The majority of discharge AD prescriptions were for an SSRI (70.6%) and in patients with history of depression and on AD prior to admission. Patients discharged on an AD were more likely to be female, of white race, and to have a prior history of cardiovascular diseases (Table). Discharge AD prescription was more common at teaching hospitals, hospitals with larger bed size and higher annual volume of ischemic stroke admissions. Amongst the patients who were not on AD prior to admission (22,437), only 8.1% were discharged on AD. Patients discharged on AD amongst AD naïve population were more likely to be female, of white race, had prior history of depression, had higher initial NIHSS and more likely to be discharged to a facility. Conclusions: Among CMS linked patients, antidepressant prescription after ischemic stroke is low and varies by key patient and hospital level characteristics. Future research examining the association between discharge AD use and patient reported outcomes after stroke is needed.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".