The Utilization of Systematic Reviews and Meta-Analyses in Stroke Guidelines
Bibliographic record
Abstract
BACKGROUND: Stroke guideline statements are important references for clinicians due to the rapidly evolving nature of treatments. Guideline statements should be informed by up-to-date systematic reviews (SRs) and meta-analyses (MAs) because they provide the highest level of evidence. To investigate the utilization of SRs/MAs in stroke management guidelines, we conducted a literature review of guidelines and extracted relevant information regarding SRs/MAs. METHODS: A literature review was conducted in PubMed with supplementation using the Trip medical database with the term "stroke" as the target population, followed by using the filter "guidelines". We extracted the number of included SRs/MAs, the years of publication, the country of origin, and other characteristics of interest. Descriptive statistics were generated using the R software version 4.2.1. RESULTS: We included 27 guideline statements. The median number of overall SRs or MAs within the guidelines was 4.0 (interquartile range [IQR] = 2-9). For MAs only, the median number included in the guidelines was 3.0 (IQR = 2.0-5.5). Canadian guidelines had the oldest citations, with a median gap of 12.0 (IQR = 5.2-18.0) years for the oldest citation, followed by European (median = 12; IQR = 9.5-13.5) and US (median = 10.0; IQR = 5.2-16) guidelines. CONCLUSIONS: Stroke guideline writing groups and issuing bodies should devote greater effort to the inclusion of up-to-date SRs/MAs in their guideline statements so that clinicians can reference recent data with the highest level of evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.312 | 0.666 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.060 | 0.053 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".