Poststroke depression and risk of stroke recurrence and mortality: protocol of a meta-analysis and systematic review
Bibliographic record
Abstract
INTRODUCTION: A number of observational studies have indicated that poststroke depression could increase the risk of stroke outcomes. There is a meta-analysis indicating that poststroke depression is a risk factor of all-cause mortality. This paper reports the protocol for a systematic review and meta-analysis to clarify the associations of poststroke depression with stroke recurrence and mortality in order to determine whether poststroke depression is a predictor of stroke outcomes according to data extracted from relevant observational studies. METHODS AND ANALYSIS: MEDLINE, Web of Science databases, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews will be used to conduct the search. Published studies written in English will be included. The risk of bias for the studies included in the systematic review or meta-analysis will be assessed by the Newcastle-Ottawa Quality Assessment Scale. HRs for stroke recurrence and mortality with 95% CIs will be included as primary outcomes. Subgroup analyses and meta-regression will be performed. ETHICS AND DISSEMINATION: Ethics approval will not be needed because the data used in this systematic review will be extracted from published studies. The results of the systematic review focusing on whether depression after stroke is a predictor for stroke recurrence and mortality will be disseminated by publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42018107944.
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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 | 0.093 | 0.135 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".