Diagnostic Accuracy of Neurological Problems 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: Previous studies describe significant rates of misdiagnosis of stroke, seizure and other neurological problems, but there are few studies examining diagnostic accuracy of all emergency referrals to a neurology service. This information could be useful in focusing the neurological education of physicians who assess and refer patients with neurological complaints in emergency departments. METHODS: All neurological consultations in the emergency department at a tertiary-care teaching hospital were recorded for six months. The initial diagnosis of the requesting physician was recorded for each patient. This was compared to the initial diagnosis of the consulting neurologist and to the final diagnosis, as determined by retrospective chart review. RESULTS: Over a six-month period, 493 neurological consultations were requested. The initial diagnosis of the requesting physician agreed with the final diagnosis in 60.4% (298/493) of cases, and disagreed or was uncertain in 35.7% of cases (19.1% and 16.6% respectively). In 3.9% of cases, the initial diagnosis of both the referring physician and the neurologist disagreed with the final diagnosis. Common misdiagnoses included neurocardiogenic syncope, peripheral vertigo, primary headache and psychogenic syndromes. Often, these were initially diagnosed as stroke or seizure. CONCLUSIONS: Our data indicate that misdiagnosis or diagnostic uncertainty occurred in over one-third of all neurological consultations in the emergency department setting. Benign neurological conditions, such as migraine, syncope and peripheral vertigo are frequently mislabeled as seizure or stroke. Educational strategies that emphasize emergent evaluation of these common conditions could improve diagnostic accuracy, and may result in better patient care.
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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 | 0.005 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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