Predictors of Poor Outcome in Patients with a Spontaneous Cerebellar Hematoma
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The authors studied the clinical and neuroimaging features of cerebellar hematomas to predict poor outcome using comprehensive statistical models. METHODS: We retrospectively reviewed clinical and neuroimaging features in 94 patients with spontaneous cerebellar hematomas to identify predictive features for a poor neurologic outcome, defined as death or dismissal to long-term care facility. Data were analyzed using chi square and Fisher's exact test with calculation of odd's ratios together with 95% confidence intervals. RESULTS: Clinical and neuroradiologic predictors for a poor outcome at p < 0.05 were admission systolic blood pressure > 200 mm Hg, hematoma size > 3 cm, visible brain stem distortion, and acute hydrocephalus. Presenting findings predicting subsequent death at p < 0.05 were abnormal corneal and oculocephalic responses, Glasgow coma sum score less than 8, motor response less than localization to pain, acute hydrocephalus and intraventricular hemorrhage. CONCLUSION: A tree-based analysis model using binary recursive partitioning showed that cornea reflex, hydrocephalus, doll's eyes, age, and size were the most important discriminating factors. Absent corneal reflexes on admission highly predicts poor outcome (86 percent, confidence limits 67-96 percent). When a cornea reflex is present, acute hydrocephalus predicts poor outcome but only when doll's eyes are additionally absent.
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 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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".