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Record W3097603276 · doi:10.1182/blood-2020-138700

Autologous Stem Cell Transplantation (ASCT) for T-Cell Non Hodgkin Lymphoma (T-NHL) Patients Who Achieve Complete Remission with First-Line Treatment: A Systematic Review and Meta-Analysis

2020· review· en· W3097603276 on OpenAlexaboutno aff
Yin Jie Koh, Louis‐Pierre Girard, Hian Li Esther Chan, Joanne Shu Xian Lee, Yen‐Lin Chee, Anand D. Jeyasekharan, Sanjay De Mel, Cinnie Yentia Soekojo, Xin Liu, Liang Piu Koh, Michelle Poon, Miny Samuel

Bibliographic record

VenueBlood · 2020
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOncologyInternal medicineAutologous stem-cell transplantationHazard ratioTransplantationMeta-analysisChemotherapy regimenProgression-free survivalSurgeryChemotherapyConfidence interval

Abstract

fetched live from OpenAlex

Introduction: There is currently no consensus on the optimal frontline therapy for patients with T cell Non-Hodgkin lymphomas (T-NHL). Consolidative autologous stem cell transplant (ASCT) is frequently offered to the patients with chemosensitive disease based on retrospective and prospective studies showing improved progression-free survival (PFS) when compared with historical controls getting chemotherapy alone. However, it remains unclear whether there is a good risk subset of patients who achieve first complete remission (CR1) following induction chemotherapy and who might not benefit from upfront ASCT. To date, no randomized control trials (RCTs) exist and available data is conflicting. We perform a systematic review/meta-analysis of the published literature to address this question. Methods A comprehensive, systematic search (from database inception - 9/2019) of MEDLINE/PubMed, EMBASE and Cochrane databases was performed. PRISMA and Meta-Analysis Of Observational Studies in Epidemiology (MOOSE) guidelines were followed. Studies were selected from a total of 2656, screened based on predefined inclusion/exclusion criteria, and were critically appraised for outcomes of interest [progression free survival (PFS) and overall survival (OS)]. Quality of studies was assessed using Newcastle-Ottawa Scale. Hazard ratios (HRs) and corresponding 95% Cis were calculated, and the meta-analysis was performed using the random-effects model. Test for heterogeneity was performed using I2 statistic. Results Of 2656 unique records, 13 studies (prospective = 3; retrospective = 10) were selected. In 8 studies, upfront ASCT was compared to NO ASCT in patients in first complete remission (CR1), while in 5, comparison was with patients achieving either PR1 (first partial remission) or CR1. 11 (of 13) studies reported PFS. Median follow-up in these studies ranged from 22 months to 7.8 years. Results from the meta-analysis showed that T-NHL patients who underwent ASCT had an improved 5-year PFS compared to those with NO ASCT (HR 1.62, 95% confidence interval (CI) 1.22 to 2.15, I² = 38%) (Figure 1). However, no benefit was observed in 5-year OS when ASCT was compared to NO ASCT (HR 1.32, 95% CI 0.82 to 2.12, I2 = 83%) (Figure 2). A sensitivity analysis including only studies with patients transplanted in CR1 showed similar findings, with a 5-year PFS (HR 1.54, 95% confidence interval (CI) 1.01 to 2.34, I² = 38%) and 5-yr OS (HR 1.11, 95% CI 0.41 to 3.02, I2 = 90%) when compared to No ASCT. Conclusions In the absence of RCTs, the results of this systematic review/meta-analysis represents the best evidence supporting long term PFS benefits of upfront ASCT consolidation in patients with T-NHL in CR1 or CR1/PR1 after frontline chemotherapy. Disclosures No relevant conflicts of interest to declare.

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)0.010
metaresearch head score (Gemma)0.021
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0190.034
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.076
GPT teacher head0.325
Teacher spread0.249 · 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
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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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