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Impact of Treatment Beyond Progression with Immune Checkpoint Blockade in Hodgkin Lymphoma

2020· article· en· W3016237720 on OpenAlexaff
Reid W. Merryman, Nicole A. Carreau, Ranjana H. Advani, Michael A. Spinner, Alex F. Herrera, Robert Chen, Sarah Tomassetti, Radhakrishnan Ramchandren, Muhammad Saad Hamid, Sarit Assouline, Raoul Santiago, Nina Wagner‐Johnston, Suman Paul, Jakub Svoboda, Steven M. Bair, Stefan K. Barta, Yang Liu, Sunita Nathan, Reem Karmali, Madelyn Burkart, Pallawi Torka, Kevin A. David, Catherine Wei, Frederick Lansigan, Lukas Emery, Daniel O. Persky, Sonali M. Smith, James Godfrey, Julio C. Chávez, Jonathan B. Cohen, Andrea B. Troxel, Catherine Diefenbach, Philippe Armand

Bibliographic record

VenueThe Oncologist · 2020
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineRetrospective cohort studyCohortBlockadeImmune checkpointInternal medicineProgression-free survivalOncologyChemotherapy

Abstract

fetched live from OpenAlex

Atypical response patterns following immune checkpoint blockade (ICB) in Hodgkin lymphoma (HL) led to the concept of continuation of treatment beyond progression (TBP); however, the longitudinal benefit of this approach is unclear. We therefore performed a retrospective analysis of 64 patients treated with ICB; 20 who received TBP (TBP cohort) and 44 who stopped ICB at initial progression (non-TBP cohort). The TBP cohort received ICB for a median of 4.7 months after initial progression and delayed subsequent treatment by a median of 6.6 months. Despite receiving more prior lines of therapy, the TBP cohort achieved longer progression-free survival with post-ICB treatment (median, 17.5 months vs. 6.1 months, p = .035) and longer time-to-subsequent treatment failure, defined as time from initial ICB progression to failure of subsequent treatment (median, 34.6 months vs. 9.9 months, p = .003). With the limitations of a retrospective study, these results support the clinical benefit of TBP with ICB for selected patients.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.336
Teacher spread0.305 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations18
Published2020
Admission routes1
Has abstractyes

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