Metastatic coagulopathic subdural hematoma: A dismal prognosis
Bibliographic record
Abstract
BACKGROUND: Dural metastases have been found in about 8-9% of patients who died of cancer, in most autopsy series. Dural metastases presenting with chronic subdural hematoma are rare, with only about 55 cases reported in the literature. CASE DESCRIPTION: We discuss the case of a 72 year old gentleman with prostate cancer who presented with a chronic subdural hematoma which was drained surgically. He was found to have disseminated intravascular coagulation (DIC) and recurrence of the subdural hematoma for which further drainage was required. After the second drainage of the chronic subdural hematoma, dural metastases were diagnosed from the pathology specimens. CONCLUSION: On reviewing the literature, 25 cases of dural metastases with chronic subdural hematoma and coagulopathy were found. These cases were characterized by the fact that they had a very poor clinical outcome in spite of surgical drainage. This combination could be a distinct entity and its recognition is important to guide management of this rare condition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".