Impact of transcranial Doppler sonography for detecting ischemic stroke
Bibliographic record
Abstract
BACKGROUND: This study aims to explore the impact of transcranial Doppler sonography (TDS) for detecting ischemic stroke (IS). METHODS: PUBMED, EMBASE, Cochrane Library, PsycINFO, Cumulative Index to Nursing and Allied Health Literature, Allied and Complementary Medicine Database, WANGFANG, Chinese Biomedical Literature Database, and China National Knowledge In-frastructure will be utilized to examine case-controlled studies that used TDS for detecting IS. All electronic databases will be searched from inception to March 20, 2020. All study selection, data extraction, and study quality assessment will be carried out by 2 independent reviewers. All study quality will be assessed by Quality Assessment of Diagnostic Accuracy Studies tool, and statistical analysis will be performed by RevMan V.5.3 software and Stata V.12.0 software. RESULTS: This study will explore the impact of TDS for detecting IS through sensitivity, specificity, positive and negative likelihood ratio, and diagnostic odds ratio. CONCLUSION: This study expects to find out whether TDS can be utilized for IS detection.Systematic review registration: INPLASY202040155.
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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 | 0.017 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".