Missed Diagnosis of Subarachnoid Hemorrhage in the Emergency Department
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Subarachnoid hemorrhage (SAH) can be devastating, yet its initial presentation may be limited to common symptoms and subtle signs, potentially leading to misdiagnosis. Little is known about population rates of misdiagnosis of SAH, or hospital factors that may contribute to it. We estimated the population-based rate of missed SAH among emergency department (ED) patients and examined its relationship with hospital characteristics. METHODS: We studied persons admitted with a nontraumatic SAH to all Ontario hospitals over 3 years (April 2002 to March 2005). SAH was defined as missed if the patient had an ED visit related to the SAH (based on a prespecified definition) in the 14 days before admission. We examined the association between hospital teaching status and missed SAH and explored whether annual ED volume of SAH or CT availability explained this association. RESULTS: Of 1507 patients diagnosed with SAH, 5.4% (95% CI, 4.3 to 6.6) had a missed diagnosis. The risk was significantly higher among patients triaged as low acuity (odds ratio 2.65; 95% CI, 1.46 to 4.80), as well as in nonteaching hospitals (adjusted odds ratio 2.12; 95% CI, 1.02, 4.44). Neither ED SAH volume nor on-site CT availability explained the effect of teaching status. CONCLUSIONS: About 1 in 20 SAH patients are missed during an ED visit. Lower acuity patients are at higher risk of misdiagnosis, suggesting the need for heightened suspicion among patients with minimal clinical findings. The risk is also greater in nonteaching hospitals, but this is not explained by the annual volume of SAHs seen in the ED or access to CT.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".