A RADIATION ONCOLOGIST’S EXPERIENCE TREATING MALIGNANT GLIOMA WITH TUMOR TREATING FIELDS IN CANADA
Bibliographic record
Abstract
Abstract The FDA approved tumor treating fields (TTFields) in 2011. Through Health Canada special access, a limited number of treatments were provided prior to regulatory approval. This work describes a single physician’s experience with TTFields. With ethics approval, charts were retrospectively reviewed. Access provided as an extension of prior research was excluded. Kaplan-Meier estimates were used for survival and time on treatment. From 2013-2022, 62 cases were identified. 65% from Quebec, 21% from Ontario and the rest from either Alberta or British Colombia. TTFields were prescribed as an adjuvant following chemo-radiation in 41 cases and for recurrent disease in 21. Median age was 49 (range 16-75). 76% of patients were male. Median KPS was 75%. 95% of tumors were classified as glioblastoma by the criteria used at the time of diagnosis (IDH positive in 4). Where known, 40% had MGMT promoter methylation. Adjuvant treatment started a median of 1.0 months following radiotherapy. Recurrent patients had had a median of 2 prior lines of treatment (range 1-9). Two young adults discontinued treatment after less than one week but otherwise patients tolerated the device until treatment was recommended to be discontinued. Median OS for adjuvant patients was 15.4 months, 5.3 months for recurrent patients. For adjuvant patients, median estimated time on treatment was 7 months. It was possible to offer TTFields over a wide geographic area with support from device specialists. Treatment may be more easily integrated into care with the development of a larger network of prescribers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".