Diagnosis of a subarachnoid hemorrhage with only mild symptoms using computed tomography in Japan
Bibliographic record
Abstract
BACKGROUND: Japan is currently an aging society, with a huge proportion of elderly citizens. Consequently, the incidence and severity of subarachnoid hemorrhage (SAH) is predicted to increase in the future. Computed tomography (CT) is very important in the initial diagnosis of SAH. The proportion of hospitals owning CT systems in Japan is around four times greater than the mean number of systems owned by hospitals in other countries belonging to the Organisation for Economic Co-operation and Development. Because CT is readily available in Japan, it follows that this technique, with its impressive diagnostic power, might be more in demand in Japan compared to other countries. However, misdiagnosis of SAH is a relatively common problem and is associated with increased mortality and morbidity, even in individuals who initially present in good condition. CASE PRESENTATION: We describe a patient with subtle clinical and CT signs of SAH. A 39-year-old Japanese man visited our hospital with a 3-day history of mild headache, shoulder stiffness, and a feeling of dizziness. His physical examination was normal aside from mild neck stiffness. Although CT did not reveal obvious abnormalities, we noticed subtle signs of SAH on CT images, which have been observed in SAH patients with mild symptoms. Thus, we diagnosed our patient with SAH and provided appropriate treatment (aneurysm clipping). Following this, the patient progressed without development of the initial complications, and he was subsequently discharged from our hospital without sequela. CONCLUSION: Thus, physicians should be able to recognize subtle characteristics of CT imaging in case of SAH patients with low grade symptoms, as this can facilitate early diagnosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".