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Record W4382501478 · doi:10.1002/hon.3197

Central nervous system lymphomas—Assessment and treatment and prevention of central nervous system relapse

2023· review· en· W4382501478 on OpenAlexaff
Paola Ghione, Katharine L. Lewis, Sabela Bobillo, Laksmi Nayak, Elisabeth Schorb, Lucia Nichelli, Andrea K. Ng, Kerry J. Savage, Pam McKay, Loretta J. Nastoupil, Carole Soussain, Kate Cwynarski

Bibliographic record

VenueHematological Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicCNS Lymphoma Diagnosis and Treatment
Canadian institutionsSpinal Cord Injury BCBC Cancer Agency
Fundersnot available
KeywordsMedicinePrimary central nervous system lymphomaLymphomaCentral nervous systemIntensive care medicinePopulationOncologyImmunologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

In this review focused on lymphoma and the central nervous system (CNS), we summarize recent developments in the management of primary (PCNSL) and secondary CNS lymphoma (SCNSL), treatment of CNS lymphoma in the older population, the neuroradiological assessment of CNS lymphoma and finally highlight the ongoing debate on optimal CNS prophylaxis. The section on PCNSL focuses on the different approaches available for frontline treatment in Europe and the United States and discusses consolidation strategies. We then highlight available strategies to treat PCNSL in the elderly population, an area of unmet need. New therapies aiming at minimizing toxicity and prioritizing quality of life are emerging for these patients. Secondary CNS lymphoma, especially in the relapsed/refractory setting is another area of unmet need, and the efficacy of CAR-T cell therapy is being explored. We provide an overview of the imaging challenges in the neuroradiological assessment of CNS lymphoma. Finally, the section on CNS prophylaxis summarizes recent findings from large retrospective studies challenging the efficacy of present approaches to prophylaxis in higher-risk patients with lymphoma.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.091
GPT teacher head0.391
Teacher spread0.300 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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