Evaluation of the impact of the Canadian subarachnoid haemorrhage clinical decision rules on British practice
Bibliographic record
Abstract
INTRODUCTION: Acute headache is among the commonest presenting complaints to emergency departments. While it is estimated that only 1-3% result from subarachnoid haemorrhage (SAH), because the disease carries such significant morbidity and mortality if missed, most clinicians have a low threshold for investigation. A recent prospective cohort study in Canada determined a number of high-risk clinical characteristics for SAH in patients with acute headache. We investigated the potential impact of incorporating the Canadian clinical decision rules on British practice. METHODS: A retrospective case note review on all adult patients presenting to our emergency department with acute headache between August and October 2011 was conducted. The Canadian decision rules for SAH were applied retrospectively to the cases identified, and the sensitivity, specificity and negative predictive values calculated. The two-tailed McNemar test was used to evaluate differences between proportions of patients undergoing investigations using the clinical decision rules against current practice. RESULTS: In all, 112 patients met the inclusion criteria in a 3-month period, of which 41 patients (36.6%) underwent unenhanced computed tomography and 4 (3.6%) were found to have SAH. Nine patients subsequently had a lumbar puncture and none demonstrated xanthochromia. None of the patients who were not fully investigated were readmitted to the regional neurosurgical centre within 6 months of discharge with missed SAH. Application of the Canadian clinical decision rules would have led to an investigation rate between 59% and 74%, compared to an actual rate of 37% (p < 0.05). CONCLUSION: The present study shows that application of the Canadian clinical decision rules for SAH would lead to more patients with acute headache being investigated than current British practice. However, much larger prospective studies are required to determine whether such clinical decision rules may identify patients at risk who would otherwise have been missed.
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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 | 0.073 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".