Diagnostic Accuracy and Application of Subarachnoid Hemorrhage Decision Rules Among Patients With Non‐Traumatic Acute Headache: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: The Ottawa and Emerald rules were developed to aid in the diagnosis of subarachnoid hemorrhage (SAH) and to determine whether a CT scan is necessary for patients presenting with non-traumatic acute headaches in the emergency department. Numerous studies have been conducted to validate these clinical decision rules. In this study, we aimed to investigate the pooled diagnostic accuracy of these rules and their impact on imaging utilization. METHODS: In this PRISMA-DTA-compliant systematic review, a comprehensive search was done in databases including PubMed, Scopus, Embase, and Web of Science. Then, screening, selection of studies, and data extraction were performed and the QUADAS-2 tool was used for critical appraisal. The true positives (TP), false negatives (FN), false positives (FP), and true negatives (TN) were extracted to calculate pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, and Diagnostic Odds Ratio (DOR) with 95% CIs. The effect of Ottawa rule on CT scan utilization was assessed by calculating pooled odds ratios for the number of CT scans in SAH and non-SAH groups before and after applying the rule. RESULTS: The pooled sensitivity, specificity, negative LR, and positive LR for the Ottawa SAH rule were 99% (95% CI: 92%-100%), 23% (95% CI: 16%-32%), 0.025 (95% CI: 0.003%-0.193%), and 1.29 (95% CI: 1.16%-1.42%) respectively. Similarly, these measures for the Emerald SAH rule were 99% (95% CI: 71%-100%), 27% (95% CI: 15%-43%), 0.065 (95% CI: 0.004%-1.072%), and 1.34 (95% CI: 1.1%-1.62%), respectively. The pooled odds ratio for CT scan utilization for the Ottawa rule was 1.15 (95% CI: 0.62%-2.13%). CONCLUSION: Both rules are highly sensitive for excluding SAH in patients with non-traumatic acute headaches presenting to the emergency department but have low specificity and do not significantly reduce CT scan utilization. TRIAL REGISTRATION: PROSPERO registration number: CRD42023476444.
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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 | 0.030 | 0.094 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".