Multiple Cerebrovascular Occlusion With Hypothyroidism: A Powerful Compensation
Bibliographic record
Abstract
Cerebral vascular occlusion can often cause severe neurological deficits. This study aims to describe a case characterized by multiple large vessel occlusions with hypothyroidism and without clinical symptoms. The patient’s blood vessel condition, compensatory pathway and perfusion situation were understood through medical history inquiry, neurological physical examination, combined with blood lipid, blood glucose, electrocardiogram, transcranial Doppler (TCD), brain computed tomography (CT), cerebral angiography, cerebral perfusion imaging and other examinations. The main symptoms of the patient were dizziness and headache, with no symptoms of neurological function loss. Digital subtraction angiography (DSA) showed multiple large vessel occlusions in the head and neck: the initial part of the left internal carotid artery, the M1 segment of the left middle cerebral artery, the V2 segment of the right vertebral artery, and the V3 terminal segment of the left vertebral artery. According to CT perfusion (CTP) examination, the blood flow (BF) value of the left temporoparietal occipital lobe was lower than that of the contralateral side, the mean blood flow passage time value was increased, and the cerebral blood volume (BV) was slightly higher than that of the contralateral side. Cerebral vascular occlusion is affected by many factors, hypothyroidism can accelerate the process of atherosclerosis, and in severe cases multiple large vessel occlusions may occur. Powerful compensatory mechanisms can reduce the incidence of stroke. J Med Cases. 2019;10(6):158-163 doi: https://doi.org/10.14740/jmc3303
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".