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Record W2089158135 · doi:10.1182/blood-2010-09-288373

How I treat relapsed and refractory Hodgkin lymphoma

2011· article· en· W2089158135 on OpenAlexaff
John Kuruvilla, Armand Keating, Michael Crump

Bibliographic record

VenueBlood · 2011
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Comprehensive Cancer NetworkMemorial Sloan-Kettering Cancer Center
KeywordsMedicineSalvage therapyContext (archaeology)Radiation therapyOncologyRefractory (planetary science)ChemotherapyLymphomaInternal medicineChemotherapy regimenRegimenDiseaseClinical trialTransplantationAutologous stem-cell transplantationSurgery

Abstract

fetched live from OpenAlex

Relapsed or refractory Hodgkin lymphoma is a challenging problem for clinicians who treat hematologic malignancies. The standard management of these patients should include the use of salvage chemotherapy followed by autologous stem cell transplant (ASCT) in patients who are chemotherapy sensitive. Open issues in this area include the role of functional imaging, the specific chemotherapy regimen to be used before ASCT, and the role of consolidative radiotherapy. Some patients will not be eligible for ASCT, and alternative approaches with conventional chemotherapy alone or with salvage radiotherapy should be considered. Prognostic factors for relapsed/refractory disease have been identified but generally are not used as a part of risk-adapted therapy. Allogeneic transplantation may offer the potential of a graft-versus-lymphoma effect, but this therapy has significant toxicity and results in few long-term disease-free survivors; hence, it should only be offered in the context of disease-specific clinical trials. An expanding list of novel drugs has exhibited promising single-agent activity. Patients have effective options beyond primary therapy, and continued progress through controlled trials remains a tangible goal in the treatment of relapsed and refractory disease.

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 score0.165
Threshold uncertainty score0.397

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 head0.026
GPT teacher head0.222
Teacher spread0.196 · 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

Citations143
Published2011
Admission routes1
Has abstractyes

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