Diagnosis and Management of Spinal Metastasis of Glioblastoma
Bibliographic record
Abstract
BACKGROUND: When patients with cranial glioblastoma develop weakness, a rare differential diagnosis is spinal metastases. METHODS: Chart and literature reviews were performed. RESULTS: The reported patient had delayed onset spinal drop metastasis that was only detected by magnetic resonance imaging (MRI). A 48-year-old patient had supratentorial glioblastoma, treated with radiotherapy (RT) and concurrent temozolomide followed by six cycles of adjuvant temozolomide. Four years after completion of all treatments (62 months from initial presentation), he developed low backache and weakness in both legs. Positron emission tomography/computed tomography scans demonstrated intracranial recurrence only. Spinal drop metastases were detected only by MRI scan. Local spinal RT 40 Gy in 20 fractions with concurrent and maintenance temozolomide were given. Because of disease progression after nine cycles of temozolomide, systemic therapy was changed to bevacizumab, which greatly improved his symptoms for 4 months before deterioration of mental status. He is still alive with disease at 22 months after diagnosis of spinal metastases (84 months from initial glioblastoma diagnosis). CONCLUSIONS: MRI is the diagnostic imaging of choice for spinal metastases. This illustrative case of delayed-onset spinal metastases shows unusual slow progression. Local RT, temozolomide, and targeted therapy may improve survival. This illustrative case is the first report of bevacizumab as a second-line therapy in drop metastasis of glioblastoma.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".