Bibliographic record
Abstract
This chapter talks about a 47-year-old Caucasian man who was referred due to memory problems and depression following surgical treatment of aortic dissection 1 year earlier. After surgery, he was unable to return to work and began receiving disability benefits. Recent laboratory tests included normal complete blood count and chemistry laboratory examinations. Neuropsychological evaluation included a neurobehavioral status examination, face-to-face neurocognitive testing, and computer-administered measures. This patient illustrates the clinical manifestations of hypoperfusive dementia and he fulfilled NINDS-AIREN diagnostic criteria for vascular dementia including: presence of dementia; evidence of stroke; and a temporal relation between the first two conditions. Despite his relatively young age, and the absence of extracranial and intracranial Cerebrovascular Disease (CVD), he had watershed infarction as a result of relentless hypotension following aortic dissection. Heavy smoking could have impaired the effective maintenance of cerebral blood flow (CBF).
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".