Subarachnoid haemorrhage rules in the decision for acute CT of the head: external validation in a UK cohort
Bibliographic record
Abstract
BACKGROUND: The Ottawa subarachnoid haemorrhage (SAH) rule and the Emerald SAH rule are clinical decision tools to aid in the decision for computed tomography (CT) of the head in patients attending an emergency department (ED) with acute non-traumatic headache. The objective of this study was to analyse the performance of these rules in a contemporary UK cohort. METHODS: We performed a retrospective external validation study. Patients undergoing CT of the head for the evaluation and treatment of non-traumatic headaches over a 6-month period in the ED at two tertiary centres were assessed. Each patient's Ottawa rule and Emerald rule were calculated and compared with their final diagnosis. RESULTS: The cohort consisted of 366 patients and there were 16 cases of SAH (based on CT findings or the presence of xanthochromia in cerebrospinal fluid). The Ottawa rule identified 288 patients requiring CT of the head. The sensitivity of the Ottawa rule was 100% (95% confidence interval (CI) 71-100%) and the specificity was 22% (95% CI 18-27%). The Emerald rule identified 267 patients who required CT, and achieved a sensitivity of 81% (95% CI 54-96%) and a specificity of 27% (95% CI 23-32%). CONCLUSIONS: The Ottawa SAH rule correctly identified all patients with SAH in this contemporary cohort. The Emerald rule did not perform as well in this cohort and is unsuitable for clinical use. The Ottawa rule is a useful tool to aid in the decision for CT of the head in patients presenting with acute non-traumatic headache to the ED.
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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 | 0.013 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".