Abstract 97: Accuracy of Prediction Instruments for Diagnosing Large Vessel Occlusion in Persons With Suspected Stroke: A Systematic Review for the 2018 AHA/ASA Guidelines for the Early Management of Patients With Acute Ischemic Stroke
Bibliographic record
Abstract
Introduction: Prediction instruments for large vessel occlusion (LVO) have been proposed to identify patients for rapid transport to endovascular thrombectomy (EVT) capable hospitals. This Evidence Review Committee was commissioned by the AHA/ASA to systematically review evidence for the accuracy of LVO prediction instruments. Methods: Medline, Embase, and Cochrane databases were searched on October 27, 2016. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy (QUADAS)-2 tool. Results: Thirty-six relevant studies were identified. Most (21/36) recruited patients with confirmed ischemic stroke, with few studies in the pre-hospital setting (4/36) and in populations that included hemorrhagic stroke or stroke mimics (12/36). Most studies had either some risk of bias or unclear risk of bias. Discrimination of LVO, as measured by the c-statistic, mostly ranged from 0.70-0.85. In meta-analysis, no threshold on any instrument predicted LVO with both high sensitivity and specificity (Table). With a positive LVO prediction test, the probability of LVO could be 50% or greater (depending on the LVO prevalence in the population), but the probability of LVO with a negative test could still be 10% or more. Conclusions: No scale predicted LVO with both high sensitivity and specificity. Systems that use LVO prediction instruments for triage will miss some patients with LVO and milder stroke. More prospective studies are needed in the pre-hospital setting in all patients with suspected stroke, including hemorrhagic stroke and stroke mimics.
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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 | 0.020 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".