Reply to A.A. Brandes et al
Bibliographic record
Abstract
We thank Brandes and Franceschi1 for their thoughtful com-ments. We agree that the Response Assessment in Neuro-Oncology (RANO) criteria will require prospective validation in clinical trials, and we indicated this need in the conclusion of the article. The primary purpose of the article was to update the response criteria for high-grade gliomas in an era in which antiangiogenic agents were producing pseudoresponses and chemoradiotherapy was causing pseudoprogression, rendering the oldMacdonald criteria ob-solete. The optimal end points for phase II and III clinical trials in high-grade gliomas were not the focus of the RANO article but will be the subject of a separate report from theRANOWorkingGroup. We agree that difficulties in assessing response and progression in patients receiving antiangiogenic therapies reduce the reliability of progression-free survival as an end point and that using overall survival as an end point has some advantages. Nonetheless, overall survival itself has its limitations, especially asmore effective salvage therapies become available.
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.
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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.035 | 0.048 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".