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Record W6949277524 · doi:10.5281/zenodo.14640141

PREreview of "Safety and Efficacy of Chimeric Antigen Receptor T-cell Therapy for Recurrent Glioblastoma: An Augmented Meta-analysis of Phase 1 Clinical Trials"

2025· peer-review· en· W6949277524 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typepeer-review
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsChimeric antigen receptorAdverse effectDiseaseCompassionate UseCAR T-cell therapyProgression-free survivalPhases of clinical researchGlioblastoma

Abstract

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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/14640141. This review is the result of a virtual, collaborative live review discussion organized and hosted by PREreview and JMIR Publications on Dec 12, 2024. The discussion was joined by 11 people: 3 facilitators, 1 member of the JMIR Publications team, and 7 live review participants including 3 who agreed to be named but did not assist in compiling the final review: Eudora Nwanaforo, Kelechi Elechi, and Murtala Haruna Bawa. The authors of this review have dedicated additional asynchronous time over the course of two weeks to help compose this final report using the notes from the Live Review. We thank all participants who contributed to the discussion and made it possible for us to provide feedback on this preprint. Summary: The study was designed to address the limitations of previous studies and evaluate the safety and efficacy of CAR T-cell therapy for recurrent glioblastoma. The results of this study are predictive rather than confirmatory. CAR T-cell therapy for glioblastoma was not predicted to significantly improve survival or achieve substantial complete responses. Stable disease rates are modest, while disease progression is notable. Adverse events, especially CAR T-cell therapy-related encephalopathy, raise safety concerns. Overall survival was 6.49 months in patients receiving CAR T-cell therapy after augmented analysis. Achieved only 80% of patients exhibiting this outcome. It was not statistically different from the median overall survival observed in recurrent glioblastoma patients undergoing standard treatment, thereby indicating that CAR T-cell therapy, in its current form, does not offer substantially improved survival compared to standard treatments. Further trials and refinements are needed to enhance CAR T-cell therapy's effectiveness and safety in glioblastoma treatment. An interesting fact is that a novel statistical technique (augmented meta-analyses) was used in this study. It was a combination of cross-sectional (quantitative) and augmented meta-analysis (qualitative) List of major concerns and feedback: Methods: Augmented Meta-analysis This section is limited in its description of the methodology used in the study. It would be helpful to include more information on the machine learning model or language model (LM) used to generate the extra cases. The title and aim specify that the study focuses on recurrent glioblastoma, but this specificity is not reflected in the inclusion criteria. It would be helpful to adjust the inclusion criteria to explicitly state that the study is targeting recurrent glioblastoma patients. This will align the methodology with the aim as stated. The inclusion criteria do not specify that patients are in phase 1 clinical trials, where safety is a primary focus. Clearly state in the inclusion criteria that patients are part of phase 1 clinical trials. This will provide context for the study's focus on safety. There is no reference to the earlier use of augmented meta-analysis in cancer or medical research, nor is it explicitly stated if this is a new application. If augmented meta-analysis has been previously applied, cite relevant references. If this is its first application, explicitly state so and highlight its novelty. Results: Literature Review and Risk of Bias Assessment section: It would be helpful to add the details of Figure 1 and Table 1 that explain the details of the cause of exclusion, the results of the Newcastle Ottawa Scale (NOS) which study reached the high-quality level, etc. Discussion: It is important to add a comparison between the mean overall survival for patients with glioblastoma who underwent CAR T-cell therapy and the median overall survival observed in patients receiving the standard protocol for recurrent glioblastoma treatment to the results section, as this comparison is mentioned in the 1st paragraph of the discussion section. Reproducibility of the study: The data presented in the study are beneficial for reproducibility except for the Augmented Meta-analysis, which is hindered by the lack of clear documentation on LLM settings. The details of the augmented meta-analysis are not available. Provide access to the source code or methodological details for augmented meta-analysis, either as supplementary material or a public repository link. Transparency will strengthen the study's reproducibility. List of minor concerns and feedback Concerns with techniques/analyses Abbreviations like IL-13Ralpha-2, EGFRvIII, HER2, and HephA2 are not identified in the included study characteristics section. Expand the abbreviations and provide their full names (e.g., Interleukin-13 Receptor Subunit Alpha-2, etc.) when first mentioned. This ensures clarity for readers not familiar with the terms. The last line of the LLM statement on page 16 does not explain how augmented meta-analysis was applied. Elaborate on how augmented meta-analysis was applied, especially in terms of methodology and integration with the study data. Figures and tables The screening section in Figure 1 is missing a rectangle to indicate the exclusion of 300 records. Update it using the PRISMA flowchart to include a rectangle that details the 300 excluded records and ensures the causes of exclusion are clearly stated. The reasons for exclusion are not detailed in the PRISMA flowchart. Follow PRISMA guidelines to specify the causes of exclusion, such as duplicates, irrelevance, or incomplete data, within the flowchart. Comments following Figure 1 are not in line with its instructions. Restructure the comments to follow the instructions and present the details of the research study accordingly. Additional comments No reference is provided for the trim-and-fill method mentioned in the augmented meta-analysis of overall survival (page 10). Cite a relevant source, such as https://doi.org/10.1097/MD.000000000015987, or another appropriate reference. The Cochrane Handbook (Part 2, Chapter 9) is advised to be referenced in the statistical analysis section and its numbered reference is cited in the text. References in the third paragraph of the introduction mix meta-analysis and clinical trials without clear distinction. Rearrange and clarify the references while ensuring that references to meta-analysis and clinical trials are grouped and contextualized appropriately to avoid confusion. Repetition of the sentence "Egger's test for publication bias could not be performed since the number of included studies in this outcome was less than ten" could be avoided by mentioning it once in the methods section as the total number of the included studies is 8 In addition, the repetition of the sentence " The wide range of the 95% confidence interval was suggestive of data sparsity, so augmented meta-analysis was indicated before making conclusions." could be avoided by mentioning it once in the augmented meta-analysis section of the methods. Concluding remarks We thank the authors of the preprint for posting their work openly for feedback. We also thank all participants of the Live Review call for their time and for engaging in the lively discussion that generated this review. Competing interests Vanessa Fairhurst was a facilitator of this call and one of the organizers. No other competing interests were declared by the reviewers.

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)0.075
metaresearch head score (Gemma)0.215
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.395

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.215
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.010
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0220.004

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 head0.263
GPT teacher head0.467
Teacher spread0.204 · 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.

Study designNot applicable
DomainEvaluation
GenreOther

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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