Prognostic Factors for Survival in Adult Patients with Cerebral Low-Grade Glioma
Bibliographic record
Abstract
Background Low-grade gliomas are considered a therapeutic dilemma because of the heterogeneity of their clinical behavior. Patients affected by these tumors are often young and management decisions should put into consideration the potential indolent nature of these tumors and the potential long-term side effects of available therapies. Aim of the Work to analyze the collective data from studies to define prognostic factors for overall survival in adult patients with cerebral low-grade gliomas. Materials and Methods We prepared this systematic review with a careful following of the Cochrane Handbook for Systematic Reviews of Interventions guidelines. We conducted a literature search till December 2020 using PubMed, Scopus, Web of Science, and Cochrane Library. We performed a search for all published articles that evaluated the impact of several factors (patient’s age, KPS, neurological symptoms, histological subtype, pre-op tumor size, tumor crossing the midline, tumor enhancement, extent of resection, radiotherapy, and chemotherapy) on the overall survival (OS) and progression-free survival (PFS) of adult patients with cerebral low-grade glioma (LGG), Studies were independently assessed for risk of bias using (Cochrane Risk of Bias 2.0) for randomized controlled trials, and (Newcastle–Ottawa Scale) for retrospective cohort studies. Results Patient-related factors (age was found significant according to 14 of total 19 studies, KPS according to 7 of total 11 studies, and presence of seizures according to 5 of total 7 studies). Tumor-related factors (tumor subtype was significant according to 10 of total 13 studies, IDH-mutation according to 4 of total 5 studies, 1p/19q codeletion according to 5 of total 5 studies, and tumor enhancement according to 4 of total 7 studies), and treatmentrelated factors (extent of resection was significant according to 18 of total 20 studies, while timing and dose of radiation, combined chemotherapy and radiotherapy failed to show major statistical significance, with better PFS in delayed radiotherapy “till progression”, and slightly better OS in combined chemo, radiotherapy). Conclusion Highly significant factors are extent of tumor resection, patient’s age, pre-op tumor volume and molecular subtype.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".