Pseudo-Subarachnoid Hemorrhage of the Head Diagnosed by Computerized Axial Tomography: A Postmortem Study of Ten Medical Examiner Cases
Bibliographic record
Abstract
In this report, we describe ten cases of pseudo-subarachnoid hemorrhage on computer axial tomography (CT) scan of the head. A pseudo-subarachnoid hemorrhage is a false positive finding by CT of the head in which the scan is interpreted as being positive for a subarachnoid hemorrhage not substantiated by subsequent neuropathologic findings. This study is a retrospective review of postmortem cases brought into the Office of the Chief Medical Examiner for the State of Maryland over a three-year period (from 1997 to 2000). We compared the clinician's impression of the CT scan with the postmortem neuropathology. The clinical diagnosis of subarachnoid hemorrhage was based on misinterpretation of non-contrast CT scans of the head. In six of the ten cases, the reading was performed by a radiologist and in four cases by nonradiologist physicians (emergency room physician, neurologist, or neurosurgeon). All the patients survived between a few hours to a few days after being admitted to the hospital. For most of the cases (80%), the neuropathology showed hypoxic/ischemic encephalopathy. The most common cause of death (four out of ten cases) was narcotic intoxication. This report is submitted so that clinicians and pathologist become more familiar with this entity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".