30 Perceived versus quantified growth trajectory of serially-imaged low-grade gliomas
Bibliographic record
Abstract
Background. Diffuse low-grade gliomas (LGGs) are infiltrative, slow-growing primary brain tumours that remain relatively asymptomatic for long periods of time before transforming into aggressive high-grade gliomas. Surveillance of tumour stability is performed primarily by serial imaging. Methods. We retrospectively identified LGG patients that were managed by observation with numerous (≥8) serial magnetic resonance imaging (MRI) studies. Tumour volumes were measured by manual segmentation on imaging. Demographic information, tumour histopathological data, and radiological interpretations were collected from electronic medical records. MRI radiology reports of tumour volume stability were classified into "growth" and "no growth" interpretations. Results. Of 74 LGG patients, 10 (13.5%) patients were included in the study. A median of 11 MRIs (range, 8-18) over a median of 79.7 months (range, 39.8-113.8 months) were analyzed per patient. Tumour diameter linearly increased at a median rate of 2.17 mm/year. Cox regression analysis showed that initial tumour volume predicted time to clinical intervention, and Mann-Whitney U test found that tumours of patients diagnosed before age 50 grew more slowly. Radiology interpretations that reported "no growth" (n=66) corresponded to a median measured growth of 3.90 mL and 11.0% compared to the comparison scan. Reports of "growth" (n=36) corresponded to median measured volume increases of 9.36 mL and 20.5%. Conclusion. We retrospectively analyzed the natural history of LGGs in serially-imaged patients at a single institution. Comparisons to the literature suggest that this is a subset of particularly slow-growing and low-risk tumours. We also highlight the clinical value of performing accurate LGG volumetric analyses.
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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 | 0.001 | 0.006 |
| 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".