CMET-22. PCV VS TEMOZOLOMIDE CHEMOTHERAPY FOR PATIENTS WITH LGG: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Low-grade glioma (LGG) encompasses a heterogeneous group of tumors that is clinically, histologically and molecularly diverse. Treatment decisions are directed toward improving upon natural history, while limiting treatment-associated toxic effects. Recent evidence has documented a utility for adjuvant chemotherapy in the management of LGG. Determine the comparative utility of temozolomide (TMZ) and procarbazine/lomustine/vincristine (PCV) for patients with LGG, particularly in the context of molecular subtype. A literature review was conducted to identify studies reporting patient response to PCV, TMZ, or a combination of chemotherapy and radiation therapy (RT). Eligibility criteria included LGG subtype, 18 < years of age, overall survival (OS), progression-free survival (PFS), and treatment course. Only Class I, II, and III data were included. 29 papers, consisting of a cohort of 3724 patients, were identified. 1p/19q codeletion, IDH1 mutation, and MGMT methylation were associated with prolonged PFS and OS. Combination therapy with PCV and RT had the highest reported PFS and OS (124.8 and 159.6 months, respectively). TMZ therapy was associated with a PFS and OS of 50.40 and 116.0 months, respectively. Treatment with TMZ was associated with less adverse effects than treatment with PCV, as manifested by the median number of treatment cycles completed (10/12 and 4.5/6, respectively). Reporting of molecular subtype data was limited. In cases with a worse natural history, such as those with intact 1p/19q and wildtype IDH1, PCV and RT may be the best option. Conversely, choosing relatively less improvement in prognosis with the gain of milder adverse treatment effects may be the better treatment course for encouraging prognostic factors, such as 1p/19q codeletion and IDH1 mutation. Our review is limited by constraints intrinsic to the literature reviewed, including different methods of reporting outcomes, treatment regimes, patient populations. Data comparison is limited by confounding variables.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".