Missed Diagnosis of Subarachnoid Hemorrhage in the Emergency Department
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 it