Validation and modification of the Ottawa subarachnoid haemorrhage rule in risk stratification of Asian Chinese patients with acute headache
Bibliographic record
Abstract
OBJECTIVE: To validate the Ottawa subarachnoid haemorrhage (SAH) rule in an Asian Chinese cohort and to explore the roles of blood pressure and vomiting in prediction of SAH in patients with nontraumatic acute headache. METHODS: A retrospective cohort study was conducted in two regional hospitals. All patients aged ≥16 years who presented with non-traumatic acute headache to the study centres from July 2013 to June 2016 were included. A logistic regression model was created for the variables of the Ottawa SAH rule and other potential predictors, including vomiting and systolic blood pressure (SBP) >160 mm Hg. Model discrimination was evaluated using the area under the receiver operating characteristic curve. Net reclassification improvement and integrated discrimination improvement indices were evaluated. The model's diagnostic characteristics, including sensitivities and specificities, were evaluated. RESULTS: A total of 500 eligible headache cases were included, in 50 of which SAH was confirmed (10%). In addition to the predictors of the Ottawa SAH rule, vomiting and SBP >160 mm Hg were found to be significant independent predictors of SAH. Net reclassification improvement and integrated discrimination improvement indices indicated that including vomiting and SBP >160 mm Hg would improve the model prediction. The Ottawa SAH rule had 94% sensitivity and 32.9% specificity. The modified Ottawa SAH rule that included both vomiting and SBP >160 mm Hg as criteria improved sensitivity to 100%, specificity to 13.1%, positive predictive value to 11.3%, and negative predictive value to 100%. CONCLUSIONS: The Ottawa SAH rule demonstrated high sensitivity. Addition of vomiting and SBP >160 mm Hg to the Ottawa SAH rule may increase its sensitivity.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".