High risk clinical characteristics for subarachnoid haemorrhage in patients with acute headache: prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To identify high risk clinical characteristics for subarachnoid haemorrhage in neurologically intact patients with headache. DESIGN: Multicentre prospective cohort study over five years. SETTING: Six university affiliated tertiary care teaching hospitals in Canada. Data collected from November 2000 until November 2005. PARTICIPANTS: Neurologically intact adults with a non-traumatic headache peaking within an hour. MAIN OUTCOME MEASURES: Subarachnoid haemorrhage, as defined by any of subarachnoid haemorrhage on computed tomography of the head, xanthochromia in the cerebrospinal fluid, or red blood cells in the final sample of cerebrospinal fluid with positive results on angiography. Physicians completed data collection forms before investigations. RESULTS: In the 1999 patients enrolled there were 130 cases of subarachnoid haemorrhage. Mean (range) age was 43.4 (16-93), 1207 (60.4%) were women, and 1546 (78.5%) reported that it was the worst headache of their life. Thirteen of the variables collected on history and three on examination were reliable and associated with subarachnoid haemorrhage. We used recursive partitioning with different combinations of these variables to create three clinical decisions rules. All had 100% (95% confidence interval 97.1% to 100.0%) sensitivity with specificities from 28.4% to 38.8%. Use of any one of these rules would have lowered rates of investigation (computed tomography, lumbar puncture, or both) from the current 82.9% to between 63.7% and 73.5%. CONCLUSION: Clinical characteristics can be predictive for subarachnoid haemorrhage. Practical and sensitive clinical decision rules can be used in patients with a headache peaking within an hour. Further study of these proposed decision rules, including prospective validation, could allow clinicians to be more selective and accurate when investigating patients with headache.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".