Fluctuating Neurologic Symptoms in a Patient With Brain Lesion
Bibliographic record
Abstract
There is a broad spectrum of focal brain lesion in humans. They include primary brain tumor, non-neoplastic cysts, metastatic lesion, stroke, vascular malformations, aneurysm, inflammatory lesions and multiple sclerosis. The case was a 45-year-old female with fluctuating visual problem, ataxia, right upper limbs weakness, and transient amnesia who was referred to the neurologist. She was alert and under treatment from 10 years before by anti-convulsive drug for seizure with unknown etiology. Physical examination revealed ataxic gate, left-sided homonymous lower quadrantanopia and loss of venous pulse in ophthalmoscopy; there were no other pathological findings. Primary brain imaging (axial brain CT scan) was performed which showed a suspected lesion in right-sided parietooccipital area. Brain MRI and CT angiography revealed a heterogeneous lesion in right-sided parietooccipital area without edema and a mass in the form of vascular malformation. Because of the size and location of the lesion, it seemed inoperable. Treatment plan included serial visit and imaging to follow the progress of lesion and probable complications such as intralesional hemorrhage and hydrocephalia. J Med Cases. 2017;8(2):67-69 doi: https://doi.org/10.14740/jmc2706w
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".