Multi-Institutional Patterns of Use of Tumor-Treating Fields for Patients with Malignant Pleural Mesothelioma
Bibliographic record
Abstract
(1) Background: The objective of this analysis was to evaluate the device usage rates and patterns of use regarding Tumor-Treating Fields (TTFields) for patients with malignant pleural mesothelioma (MPM) throughout the US. (2) Methods: We evaluated de-identified data from 33 patients with MPM enrolled in FDA-required HDE protocols at 14 institutions across the US from September 2019 to March 2022. (3) Results: The median number of total TTFields usage days was 72 (range: 6–649 days), and the total treatment duration was 160 months for all patients. A low usage rate (defined as less than 6 h per day, 25%) was observed in 34 (21.2%) months. The median TTFields usage in the first 3 months was 12 h per day (range: 1.9–21.6 h), representing 50% (range: 8–90%) of the potential daily duration. The median TTFields usage after 3 months decreased to 9.1 h per day (range: 3.1–17 h), representing 38% (range: 13–71%) of the daily duration, and was lower than usage in the first 3 months (p = 0.01). (4) Conclusions: This study represents the first multicenter analysis of real-world TTFields usage based on usage patterns for MPM patients in clinical practice. The real-world usage level was lower than the suggested daily usage. Further initiatives and guidelines should be developed to evaluate the impact of this finding on tumor control.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".