P01.145 Tumor growth dynamics in serially-imaged low-grade glioma patients
Bibliographic record
Abstract
Diffuse low-grade gliomas (LGGs) are infiltrative, slow-growing primary brain tumors that remain relatively asymptomatic for long periods of time before progressing into aggressive and fatal high-grade gliomas. We retrospectively identified LGG patients with numerous (>= 8) serial magnetic resonance imaging (MRI) studies. Tumor volumes were measured by manual segmentation on serial imaging to study the natural history and growth of the lesion. Patient demographic information, tumor characteristics, and histological data were collected from electronic medical records and paper charts. Out of 74 LGG patients, 10 patients (13.5%) were identified to meet the study criteria with number of MRIs acquired ranging from 8 to 18 (median, 11.5) over a median of 79.7 months (range, 39.8–113.8 months). Tumor diameter increased at a median of 2.17 mm/year in a linear trajectory. Cox regression analysis revealed that initial tumor volume was an independent predictor of time to clinical intervention, and Mann-Witney U test found that patients younger than 50 years old had significantly slower-growing tumors. Clinical intervention was more likely for tumors above a volume threshold of 73.8 mL. We retrospectively analyzed the natural history of LGGs of patients managed at a single institution with numerous serial MRI scans. Comparisons of our cohort to the literature suggest that this is a subset of particularly slow-growing and low-risk tumors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".