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Record W4413105867 · doi:10.1093/noajnl/vdaf123.116

RMTD-10 THERAPIES FOR LEPTOMENINGEAL METASTATIC DISEASE: A NETWORK META-ANALYSIS

2025· article· en· W4413105867 on OpenAlexaff
Vinay Suresh, Suhrud Panchawagh, Muneeb Ahmad Muneer, Alireza Mansouri, Ahmad Ozair

Bibliographic record

VenueNeuro-Oncology Advances · 2025
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMedicineOncologyInternal medicineMeta-analysisConfidence intervalRandomized controlled trialHazard ratioRadiation therapy

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION Leptomeningeal metastatic disease (LMD) literature has had extremely limited head-to-head comparison of available therapies, including systemic chemotherapy (SysCT), intrathecal (IT) or intraventricular therapy (ITV), whole-brain radiotherapy (WBRT), or cranio-spinal irradiation (CSI). No network meta-analysis (NMA) of randomized controlled trials (RCTs) of LMD has been reported so far. METHODS NMA was conducted using published LMD RCTs and reported following PRISMA-NMA guidelines. Primary outcome was survival, assessed using hazard ratios (HRs) and 95% confidence intervals (95%CI). Survival times were converted to HRs under exponential distribution assumption. LogHR standard errors were calculated using deaths per RCT arm. P-scores were used to estimate probability of treatment being superior. Analysis was performed in R using frequentist approach, followed by back-transforming logHRs for indirect/direct comparisons. Incremental effects were estimated using additive model. Analyses were performed separately for main subnetwork, with disconnected subnetworks accommodated in additive model. RESULTS Seven studies were included, incorporating 7 pairwise comparisons and 9 treatments. 3 subnetworks were identified. Primary subnetwork analysis included 5 studies. Compared to SysCT±RT (reference), logHRs (95%CI) for survival were: IT/ITV DepoCyt±SysCT±RT -0.094(-0.468,0.281); IT/ITV methotrexate(MTX)±cytarabine(AraC)±SysCT±RT 0.8567(0.066,1.647). IT/ITV MTX±SysCT±RT 0.318(-0.163,0.798); ITV Thiotepa±SysCT±RT 0.438(-0.294,1.169). Heterogeneity and inconsistency were not detected (τ²=0,I²=0%,Q-statistic=0.57,P=0.45). P-scores were highest for IT/ITV DepoCyt±SysCT±RT (0.894), followed by SysCT±RT (0.769). Disconnected-component-NMA demonstrated incremental logHR: AraC 0.539(-0.347,1.425); IT/ITV DepoCyt 0.052(-0.566,0.670); IT/ITV MTX 0.179(-0.484,0.840); ITV thiotepa 0.298(-0.767,1.362); proton CSI -0.250(-0.726,0.225). No component demonstrated statistically significant incremental benefit, with moderate heterogeneity and adequate fit present (τ²=0.1017,I²=57%,Q-statistic=4.65,P=0.098). CONCLUSIONS IT/ITV DepoCyt±SysCT±RT and SysCT±RT were found most effective. A lack of significant incremental benefit warrants evaluating older therapies in contemporary RCTs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.026
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0130.045
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.068
GPT teacher head0.394
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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