P.043 Volumetric analysis of low-grade glioma growth in serially-imaged patients
Bibliographic record
Abstract
Background: Diffuse low-grade gliomas (LGGs) are infiltrative, slow-growing primary brain tumors that remain relatively asymptomatic for long periods of time before progressing to aggressive high-grade gliomas. Methods: We retrospectively identified LGG patients that were stably managed by observation with numerous (≥ 8) serial magnetic resonance imaging (MRI) studies. Tumour volumes were measured by manual segmentation on imaging to study the growth of the lesion. Patient demographic information, tumour characteristics, and histological data were collected from electronic medical records. Results: Of 74 LGG patients, 10 (13.5%) patients were included in the study. The number of MRIs acquired ranged from 8 to 18 (median, 11) over a median of 79.7 months (range, 39.8-113.8 months). Tumor diameter increased at a median rate of 2.17 mm/year in a linear trajectory. Cox regression analysis revealed that initial tumour volume predicted time to clinical intervention, and Mann-Whitney U test found that patients diagnosed prior to age 50 had significantly slower-growing tumors. Clinical intervention was more likely for tumours larger than 73.8 mL. Conclusions: We retrospectively analyzed the natural history of LGGs in patients with numerous serial MRIs managed at a single institution. Comparisons to the literature suggest that this is a subset of particularly slow-growing and low-risk tumours.
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 | 0.000 | 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".