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Record W4225612 · doi:10.1520/jfs2001099

Pseudo-Subarachnoid Hemorrhage of the Head Diagnosed by Computerized Axial Tomography: A Postmortem Study of Ten Medical Examiner Cases

2002· article· en· W4225612 on OpenAlexaff
DJ Chute, Smialek Je

Bibliographic record

VenueJournal of Forensic Sciences · 2002
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsSubarachnoid hemorrhageMedicineMedical examinerHead (geology)TomographyForensic pathologyRadiologyPoison controlNuclear medicineAutopsyInjury preventionMedical emergencySurgeryPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.304
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2002
Admission routes1
Has abstractyes

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