VALIDATION OF RISK STRATIFICATION MODELS FOR LONE ACUTE SUDDEN HEADACHE (LASH)—HOW FAR HAVE WE TRAVELLED?
Bibliographic record
Abstract
Objectives & Background Lone acute sudden headache (LASH) remains a challenging presentation for the emergency physician (EP). Maintaining safe practice and yet balancing the risks and benefits of potential over investigation have generated the development of risk stratification models to help identify patients with serious pathology such as subarachnoid haemorrhage SAH). The Ottawa SAH rule has potential to reduce the need for radiological intervention and lumbar puncture in certain groups of patients but has not been validated in a UK setting. To validate the Ottawa SAH rules in a UK emergency setting and assess its potential performance. Methods A structured data retrieval tool was developed to perform a retrospective medical record review for all patients admitted to the Clinical Decision Units of two Emergency Departments (EDs) with severe headache, over a 12 month period. The Ottawa (OSAH) rules were then applied to this data set. Results Of the 1096 patients recruited to the study, 890(81.2%) had a CT scan performed due to a consideration of LASH to exclude SAH. Of patients with a normal CT, only 346 had a lumbar puncture (LP) performed due to patient choice or clinician decision making. A total of 15 patients had SAH confirmed (1.37%) – all on non contrast CT. Applying the OSAH rules would have identified all 15 patients with SAH (100% sensitivity). There was also the potential to decrease the number of LPs performed by 27%, 37% and 44% for each clinical risk tool. Conclusion The OSAH rule is a useful adjunct to clinical decision making in patients presenting to ED with LASH in a UK population. The EP can also use data from studies like ours (the largest single centre study in the UK to date) to help in joint decision making with patients to help quantify risk and appropriateness of need for further investigation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".