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PB1792 AGE AS AN INDEPENDENT PREDICTIVE FACTOR IN SURVIVAL OUTCOMES OF MULTIMODALITY TREATMENTS FOR PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOMA: A REVIEW FROM TERTIARY INSTITUTION

2019· review· en· W2951024782 on OpenAlexaff
Jin Su Song, Rajiv Samant, Xiaohui Fan, Mohammad Jay, Hina Chaudry, David MacDonald, I. Bence‐Bruckler, V. M. G. Nair

Bibliographic record

VenueHemaSphere · 2019
Typereview
Languageen
FieldMedicine
TopicCNS Lymphoma Diagnosis and Treatment
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicinePrimary central nervous system lymphomaRadiation therapyProportional hazards modelHazard ratioInternal medicineSalvage therapySurvival analysisRetrospective cohort studyMultivariate analysisLymphomaLog-rank testSurgeryChemotherapyConfidence interval

Abstract

fetched live from OpenAlex

Background: Primary central nervous system lymphoma (PCNSL) is a rare malignancy with a median survival of less than 3 months, if untreated. Multimodality treatments with high‐dose methotrexate (HD‐MTX)‐based systemic therapy and/or whole brain irradiation for consolidation or salvage constitutes the most commonly used treatment approach. Due to severe treatment toxicity and aggressive course of the disease, not all patients benefit from this treatment approach. Aims: In this retrospective study, we aimed to identify various clinical parameters that predicted outcomes on survival, and response to various treatments in patients with PCNSL. Methods: Patients diagnosed with PCNSL between 2002 and 2017 were selected for analysis. Data on patient demographics, tumor characteristics and treatment were collected and analyzed for correlation with clinical outcomes. Survival curves were generated with the Kaplan‐Meier method and compared using log‐rank test. Multivariate analysis was performed where prognostic variables and patient outcome were correlated with Cox proportional hazard model. Results: A total of 82 patients were identified and selected for analysis. Median age at diagnosis was 68 years (range 30‐89 years) and median follow up was 3.7 years. The majority (86.6%) of tumors were identified as diffuse large B‐cell lymphoma on histology. Among the 82 patients, 10 (12.2%), 31 (37.8%) and 23 (28.0%) patients received systemic therapy (CT) only, radiotherapy (RT) only and systemic therapy followed by salvage radiotherapy (CRT), respectively, while 18 (22.0%) patients received supportive care (SC) only. Median time interval between diagnosis and treatment was 33 days for CT group and 63 days for RT group. Median overall survival (OS) of the entire cohort was 11.1 months (95% CI 6.1‐15.5 months), while median OS for RT, CRT and SC groups were 8.8 months (95% CI 4.5‐11.3 months), 30.1 months (95% CI 19.3‐41.0 months) and 3.3 months (95% CI 0.8‐5.8 months), respectively (median OS for CT group not reached). Multivariate analysis demonstrated that both the use of systemic therapy (hazard ratio [HR] 0.23, 95% CI 0.11‐0.49, p < 0.001) and radiotherapy (HR 0.54, 95% CI 0.32‐0.92, p = 0.022) were associated with improved survival in the total population, while age (p = 0.48) or type of tumor (p = 0.88) did not demonstrate any statistical significance. Subgroup analysis showed that systemic therapy in patients younger than 70 years of age was associated with improved OS (HR 0.13, 95% CI 0.05‐0.32, p < 0.001), whereas in elderly patient population (70 years of age or older), addition of radiotherapy was associated with improved OS (HR 0.45, 95% CI 0.21‐0.96, p = 0.039). Summary/Conclusion: Our results concur with the published literature demonstrating the survival benefit with the use of systemic therapy in younger patient population. Radiotherapy was independently associated with an improved overall survival in older patient population and therefore should be considered as palliative treatment of choice in the elderly population who may not be candidates for systemic therapy. Further prospective studies are required to validate our findings as well as optimization of radiotherapy in this population.

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 score0.831
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
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 head0.055
GPT teacher head0.346
Teacher spread0.291 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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