The benefit of early surgery on overall survival in incidental low-grade glioma patients: A multicenter study
Bibliographic record
Abstract
BACKGROUND: The role of surgery for incidentally discovered diffuse incidental low-grade gliomas (iLGGs) is debatable and poorly documented in current literature. OBJECTIVE: The aim was to identify factors that influence survival for patients that underwent surgical resection of iLGGs in a large multicenter population. METHODS: Clinical, radiological, and surgical data were retrospectively analyzed in 267 patients operated for iLGG from 4 neurosurgical Centers. Univariate and multivariate analyses were performed to identify predictors of overall survival (OS) and tumor recurrence (TR). RESULTS: The OS rate was 92.41%. The 5- and 10-year estimated OS rates were 98.09% and 93.2%, respectively. OS was significantly longer for patients with a lower preoperative tumor volume (P = .001) and higher extent of resection (EOR) (P = .037), regardless the WHO-defined molecular class (P = .2). In the final model, OS was influenced only by the preoperative tumor volume (P = .006), while TR by early surgery (P = .028). A negative association was found between preoperative tumor volumes and EOR (rs = -0.44, P < .001). The median preoperative tumor volume was 15 cm3. The median EOR was 95%. Total or supratotal resection of T2-FLAIR abnormality was achieved in 61.62% of cases. Second surgery was performed in 26.22%. The median time between surgeries was 5.5 years. Histological evolution to high-grade glioma was detected in 22.85% of cases (16/70). Permanent mild deficits were observed in 3.08% of cases. CONCLUSIONS: This multicenter study confirms the results of previous studies investigating surgical management of iLGGs and thereby strengthens the evidence in favor of early surgery for these lesions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".