In Reply: A Data-Driven Approach to Predicting 5-Aminolevulinic Acid-Induced Fluorescence and World Health Organization Grade in Newly Diagnosed Diffuse Gliomas
Bibliographic record
Abstract
To the Editor: We greatly appreciate the authors' letter1 regarding our publication “A Data-Driven Approach to Predicting 5-Aminolevulinic Acid-Induced Fluorescence and World Health Organization Grade in Newly Diagnosed Diffuse Gliomas.” Contrast enhancement is a relevant predictor of intraoperative 5-aminolevulinic acid (5-ALA) fluorescence in our logistic regression–based mode. Our observations can be explained by the fact that the brain-blood barrier in diffuse lower-grade gliomas (DLGG) is semipermeable to 5-ALA.2 Our group has shown that higher 5-ALA doses result in more protoporphyrin IX conversion, an observation with implications for future dosing in DLGG.3 Having given our last analysis further thought under present circumstances, however, the question we were asking could perfectly be addressed using the machine learning pattern recognition, allowing to potentially incorporate multidimensional factors beyond contrast enhancement for predicting intraoperative 5-ALA fluorescence in a more holistic way than our older analyses. Our group is currently working on several such projects to predict intraoperative 5-ALA fluorescence in DLGG. Solid prediction modeling may translate into lower costs by identifying those patients in whom application of 5-ALA will not lead to intraoperative fluorescence as observed by the surgeon. At the same time, however, reporting guidelines need to be respected to maintain the high quality of research and higher yields of prediction.4 As stated by Barrie and Detchou, it would definitely be desirable to corroborate our findings in a prospective study setting. We are in the active stage of conceptualizing a sampling study on tumor heterogeneity as depicted by 5-ALA fluorescence and amino acid PET using machine learning algorithms, in diffuse lower-grade gliomas. Finally, we again thank the authors for giving us the chance of raising additional points on prediction of 5-ALA fluorescence in DLGG.
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 | 0.009 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".