Summary of the best evidence for screening and management of post⁃stroke depression in adults
Bibliographic record
Abstract
ObjectiveTo obtain relevant evidence for the screening and management of post-stroke depression in adults at home and abroad，and to summarize the best evidence.MethodsEvidence，including best practices，guidelines，evidence summary，systematic reviews，expert consensus，clinical decisions related to post⁃stroke depression in adults were retrieved from British Medical Journal Best Clinical Practice，Up to Date，World Health Organization，International Guidelines Collaboration，NICE Guidelines，Scottish Intercollegiate Guideline Network （SIGN），National Guideline Clearing⁃house（NGC），Europe Stroke Association，American Stroke Association（ASA），Australian Stroke Foundation，Heart and Stroke Foundation of Canada（HSFC），Registered Nurses' Association of Ontario（RNAO），Joanna Briggs Institute Evidence⁃Based Practice Database，Cochrane Library，CINAHL，PubMed，China Clinical Guidelines Network，China National Knowledge Infrastructure（CNKI），and China Biology Medicine disc from January 1，2010 to October 31，2019.ResultsA total of 15 articles were included，involving 1 clinical decision，2 best practices，5 guidelines，6 evidence summaries，and 1 expert consensus.The best evidence included 47 items involved in screening，evaluation，prevention，management，health education of post⁃stroke depression.ConclusionsHealth care practitioners working with post⁃stroke patients should be aware of the severity of post⁃stroke depression，master correct depression screening and management methods，and reduce the incidence of post⁃stroke depression.
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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 | 0.014 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".