NovoTTF-100A alternating electric fields therapy for recurrent glioblastoma: An analysis of patient registry data
Bibliographic record
Abstract
This study sought to identify major predictors of survival after second surgery.Methods: We collected clinical, pathological and radiographic data through a retrospective review of charts of 21 patients who underwent elective surgery for GBM recurrence at our institution in the past 6 years.Kaplan-Meier survival analysis and Cox proportional-hazards regression were employed to determine which variables significantly impacted survival time.Results Among variables examined, age, less than or equal to 50 (P equals 0.04), and chemotherapy treatment after second surgery (P equals 0.00057), were significant.Patients younger than 50, had a mean length of survival period of 14.7 months, while patients, age 50 or older, survived an average of 7.6 months.Patients who underwent chemotherapy after second resection survived an average of 12.6 months.Comparatively, mean survival period of patients who did not undergo chemotherapy was 3.7 months.The cumulative prognostic significance of age and post-reoperative chemotherapy treatment was determined to be 0.038 using Cox proportional-hazards regression modelling.Conclusion: The results confirm that younger patients survive longer after second surgery and that a second round of chemotherapy can prolong survival.Data from larger cohorts of patients is required to identify other important predictors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".