Diagnostic Accuracy of Neurological Problems in the Emergency Department
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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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 | 0.003 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".