Clinical trial end points for high-grade glioma: the evolving landscape
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To review the strengths and weaknesses of primary and auxiliary end points for clinical trials among patients with high-grade glioma (HGG). Recent advances in outcome for patients with newly diagnosed and recurrent HGG, coupled with the development of multiple promising therapeutics with myriad antitumor actions, have led to significant growth in the number of clinical trials for patients with HGG. Appropriate clinical trial design and the incorporation of optimal end points are imperative to efficiently and effectively evaluate such agents and continue to advance outcome. Growing recognition of limitations weakening the reliability of traditional clinical trial primary end points has generated increasing uncertainty of how best to evaluate promising therapeutics for patients with HGG. The phenomena of pseudoprogression and pseudoresponse have made imaging-based end points, including overall radiographic response and progression-free survival, problematic. Although overall survival is considered the "gold-standard" end point, recently identified active salvage therapies such as bevacizumab may diminish the association between presalvage therapy and overall survival. Finally, advances in imaging as well as the assessment of patient function and well being have strengthened interest in auxiliary end points assessing these aspects of patient care and outcome. Better appreciation of the strengths and limitations of primary end points will lead to more effective clinical trial strategies. Technical advances in imaging as well as improved survival for patients with HGG support the further development of auxiliary end points evaluating novel imaging approaches as well as measures of patient function and well being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it