Validation of the Ottawa Subarachnoid Hemorrhage Rule in patients with acute headache
Bibliographic record
Abstract
BACKGROUND: We previously derived the Ottawa Subarachnoid Hemorrhage Rule to identify subarachnoid hemorrhage (SAH) in patients with acute headache. Our objective was to validate the rule in a new cohort of consecutive patients who visited an emergency department. METHODS: We conducted a multicentre prospective cohort study at 6 university-affiliated tertiary-care hospital emergency departments in Canada from January 2010 to January 2014. We included alert, neurologically intact adult patients with a headache peaking within 1 hour of onset. Treating physicians in the emergency department explicitly scored the rule before investigations were started. We defined subarachnoid hemorrhage as detection of any of the following: subarachnoid blood visible upon computed tomography of the head (from the final report by the local radiologist); xanthochromia in the cerebrospinal fluid (by visual inspection); or the presence of erythrocytes (> 1 × 106/L) in the final tube of cerebrospinal fluid, with an aneurysm or arteriovenous malformation visible upon cerebral angiography. We calculated sensitivity and specificity of the Ottawa SAH Rule for detecting or ruling out subarachnoid hemorrhage. RESULTS: Treating physicians enrolled 1153 of 1743 (66.2%) potentially eligible patients, including 67 with subarachnoid hemorrhage. The Ottawa SAH Rule had 100% sensitivity (95% confidence interval [CI] 94.6%–100%) with a specificity of 13.6% (95% CI 13.1%–15.8%), whereas neuroimaging rates remained similar (about 87%). INTERPRETATION: We found that the Ottawa SAH Rule was sensitive for identifying subarachnoid hemorrhage in otherwise alert and neurologically intact patients. We believe that the Ottawa SAH Rule can be used to rule out this serious diagnosis, thereby decreasing the number of cases missed while constraining rates of neuroimaging.
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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 | 0.008 | 0.036 |
| 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 | 0.001 |
| 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".