Pattern of cerebellar perfusion on single photon emission computed tomography in subcortical hematoma: A clinical and computed tomography correlation
Bibliographic record
Abstract
BACKGROUND: There is paucity of studies evaluating the role of asymmetry index (AI) on single photon emission computed tomography (SPECT) studies in patients with intracerebral hemorrhage (ICH). AIM: To evaluate cerebellar perfusion in ICH employing SPECT study and correlate with clinical and CT scan findings. SETTING AND DESIGN: Tertiary care teaching hospital. MATERIALS AND METHODS: A total of 29 patients with ICH were subjected to neurological examination including Glasgow Coma Scale (GCS) and Canadian Neurological Stroke Scale (CNS). Clinical features of raised intracranial pressure and herniation were noted. On CT scan, ICH location, volume, ventricular extension and midline (ML) shift were noted. On SPECT, cerebral and cerebellar perfusion was measured semiquantitatively and AI calculated. Outcome was defined at 3 months into poor and good. RESULTS: Fourteen patients had putaminal and 15 thalamic hemorrhages. Their mean age was 59 years. The mean GCS score was 10 and CNS score 2.8. Hematoma was large in five, medium in 16 and small in eight patients. ML shift was present in 15 and hematoma extended to ventricule in 16 patients. On SPECT, cerebellar AI significantly related to ML shift but not with size of hematoma. AI was low in patients with ML shift. Outcome was related to GCS score, ML shift, size of hematoma and cerebellar AI. CONCLUSION: In acute stage of ICH, cerebellar AI is lower in patients with more severe stroke having ML shift.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".