NCOG-22. TIGER PRO-ACTIVE STUDY: INVESTIGATING DAILY ACTIVITY, SLEEP AND NEUROCOGNITIVE FUNCTIONING IN GLIOBLASTOMA PATIENTS APPLYING TTFIELDS THERAPY IN GERMANY IN ROUTINE CLINICAL CARE
Bibliographic record
Abstract
Abstract Based on the EF-14 trial, Tumor Treating Fields (TTFields) therapy is recommended in treatment guidelines for newly diagnosed glioblastoma (GBM). However, real-world data are limited. The non-interventional TIGER study (NCT03258021) will give insight into patients’ therapy decision, TTFields therapy duration and usage, quality of life, overall survival, and adverse events (AEs) in the real-world setting. Subsequently, we have now established the TIGER PROgram, a collaborative national network of neurooncology centers, that will allow us to use previously established administrative structures for new real-world investigations. Also, it will enable a basic data set across different trials and thereby facilitate meta-analyses across different populations. Here we report on the first study that we initiated within this program, the TIGER PRO-Active Study (NCT04717739), a prospective, an ongoing non-interventional, multicenter study in Germany investigating changes in daily activity, sleep, neurocognitive functioning as potential quality of life parameter in GBM patients whilst receiving TTFields therapy. Furthermore, TTFields therapy usage and serious AEs will be evaluated. Based on the planned subgroup analysis (MGMT and age), the experiences with the ongoing TIGER trial, the expected dropout rate and in consideration of the Central Limit Theorem (sufficiently large group size of n ≥ 30) we will recruit approximately 500 adult patients with newly diagnosed GBM over the course of 2 years. Data on physical activity and sleep will be collected via specific smartphone apps. Neurocognitive functioning will be assessed using the MoCA (Montreal Cognitive Assessment) interview test and quality of life with the EORTC QLQ-C30 and -BN20 questionnaires. Data will be collected over a time period of at least 12 months. First results are expected in 2024.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".