Outpatient follow-up of stroke patients: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To summarise the best evidence of outpatient follow-up of stroke patients, and to provide a reference for clinical practice. Methods: The current systematic review was conducted in November 2024 in China and comprised literature search done across a range of databases, including Guideline International Network, National Guideline Clearinghouse, China Guideline Clearinghouse, National Institute for Clinical Medicine, Joanna Briggs Institute Evidence-based Nursing Centre Library, Best Practice, Registered Nurses' Association of Ontario, PubMed, Excerpta Medica dataBASE, Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure, WanFang Data and China Biology Medicine disc. The search targetted studies reporting data on the outpatient follow-up of patients with cerebral apoplexy. RESULTS: Of the 14 studies, 1(7.14%) involved clinical decision-making, 7(50%) were guidelines, and 2(14.28%) each were systematic reviews, expert consensus, and randomised controlled trials. The studies identified 33 best pieces of evidence across 6 categories for outpatient follow-up of stroke survivors after discharge. Conclusion: The review summarised the best pieces of evidence and provided a reference for standardised outpatient follow-up for stroke patients.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".