P.021 Surgical management of incidentally discovered diffusely infiltrating glioma
Bibliographic record
Abstract
Background: Occasionally low grade gliomas (LGGs) are identified incidentally while asymptomatic. The diagnosis of incidental LGGs has become more frequent due to increase in access to medical imaging. While management of these lesions remains controversial, early surgery has been suggested to improve outcome. Methods: All LGGs treated between 2004 and 2016 at our institution were reviewed. Patients with incidentally discovered glioma were identified and retrospectively reviewed. “Incidental” was defined as an abnormality on imaging that was obtained for a reason not attributable to the glioma. Outcomes were measured by overall survival, progression free survival and malignant progression free survival. Results: Thirty-four out of 501 adult patients who were treated for low grade glioma were discovered incidentally. Headache (26%, n=9) and screening (21%, n=7) were the most common indications for brain imaging. The mean duration follow up was 5 years. Twelve patients had disease progression, 5 cases of malignant progression and 4 deaths. Oligodendroglioma was diagnosed in 16 and astrocytoma in 15 patients. Twenty-five (74%) patients had IDH1 mutation and demonstrated prolonged survival. Conclusions: This retrospective cohort of incidentally discovered LGGs were surgically removed with minimal surgical risk. There is improved overall survival likely attributable to the underlying favorable biology of the disease indicated by the presence of IDH1 mutation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".