Prophylactic Anticonvulsants in Patients with Brain Tumour
Bibliographic record
Abstract
OBJECTIVE: We conducted a clinical trial to determine if prophylactic anticonvulsants in brain tumour patients (without prior seizures) reduced seizure frequency. We stopped accrual at 100 patients on the basis of the interim analysis. METHODS: One hundred newly diagnosed brain tumour patients received anticonvulsants (AC Group) or not (No AC Group) in this prospective randomized unblinded study. Sixty patients had metastatic, and 40 had primary brain tumours. Forty-six (46%) patients were randomized to the AC Group and 54 (54%) to the No AC Group. Median follow-up was 5.44 months (range 0.13-30.1 months). RESULTS: Seizures occurred in 26 (26%) patients, eleven in the AC Group and 15 in the No AC Group. Seizure-free survivals were not different; at three months 87% of the AC Group and 90% of the No AC Group were seizure-free (log rank test, p = 0.98). Seventy patients died (unrelated to seizures) and survival rates were equivalent in both groups (median survival = 6.8 months versus 5.6 months, respectively; log rank test, p = 0.50). We then terminated accrual at 100 patients because seizure and survival rates were much lower than expected; we would need > or = 900 patients to have a suitably powered study. CONCLUSIONS: These data should be used by individuals contemplating a clinical trial to determine if prophylactic anticonvulsants are effective in subsets of brain tumour patients (e.g. only anaplastic astrocytomas). When taken together with the results of a similar randomized trial, prophylactic anticonvulsants are unlikely to be effective or useful in brain tumour patients who have not had a seizure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".