Dexamethasone and compliance affect TTFields efficacy to glioblastoma patients: a systematic review and meta-analysis
Bibliographic record
Abstract
TTFields is a novel treating modality of glioblastoma (GBM) which can significantly prolong the overall survival (OS) of newly diagnosed or recurrent glioblastoma. Some researchers have revealed that a variety of factors can affect the efficacy of TTFields. So, we review the available literature about the influencing factors on efficacy of TTFields and then choose two experimentally supported factors: the dose of dexamethasone and compliance of TTFields to perform a meta-analysis. The PubMed, Embase, and the Cochrane Library are searched. Five articles are identified between 2014 and 2017. Three articles are about the compliance of TTFields. Two articles are about the dose of dexamethasone. The Newcastle-Ottawa Quality Assessment Scale (NOS) is used as an assessment tool to evaluate the methodological quality of all included trials. The scale's range varies from 0 to 9 stars. According to the Cochrane Handbook for Systematic Reviews of Interventions, articles are graded in six items to evaluate the risk of bias. Two reviewers rate the studies independently and the final decision is reached by consensus.Our data shows that the median OS is conspicuously longer in the TTFields group in which the dose of dexamethasone is ≤ 4.1 mg, WMD = 9.23 [95% CI 5.69-12.78]; P < 0.05). And the patients whose compliance of TTFields treatment ≥ 75% (≥ 18 h per day) have a significant lower overall survival risk than the patients whose compliance of TTFields treatment < 75% (HR = 0.57 [95% CI 0.46-0.70]; P < 0.00001).TTFields is a safe and efficient novel treatment modality. The dose of dexamethasone ≤ 4.1 mg of TTFields treatment and the compliance of TTFields treatment ≥ 75%, ≥ 18 h per day are beneficial to the prognosis of the glioblastoma patients.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.031 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".