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Record W3144497774 · doi:10.1002/pbc.28562

Pediatric classical Hodgkin lymphoma

2021· review· en· W3144497774 on OpenAlexaff
Andrea Lo, Karin Dieckmann, Tanja Pelz, Eve Gallop‐Evans, Rita Engenhart‐Cabillic, Dirk Vordermark, Kara M. Kelly, Cindy L. Schwartz, Louis S. Constine, Kenneth B. Roberts, David Hodgson

Bibliographic record

VenuePediatric Blood & Cancer · 2021
Typereview
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of British ColumbiaUniversity of TorontoBC Cancer Agency
Fundersnot available
KeywordsMedicineOncologyCogChemotherapyProcarbazineLymphomaDiseaseInternal medicineHodgkin lymphomaClinical trialVincristineCyclophosphamide

Abstract

fetched live from OpenAlex

Over the past century, classical Hodgkin lymphoma (HL) has been transformed from a uniformly fatal disease to one of the most curable cancers. Given the high cure rate, a major focus of classical HL management is reducing the use of radiation therapy (RT) and chemotherapy agents such as procarbazine and doxorubicin to minimize long-term toxicities. In both North America and Europe, an important philosophy in the management of classical HL is to guide the intensity of treatment according to the risk category of the disease. The main factors used for risk classification are tumor stage, bulk of disease, and the presence of B symptoms. Response to chemotherapy is an important factor guiding the utilization of RT in ongoing Children's Oncology Group (COG) and European Network Pediatric Hodgkin Lymphoma (EuroNet-PHL) trials. Both trial groups have transitioned to reduced RT volumes that target the highest risk sites using highly conformal techniques, along with standard or intensified chemotherapy regimens to improve outcomes in higher risk patients. However, given the potential acute toxicities of intensified chemotherapy, immunoregulatory drugs are being investigated in upcoming trials. The purpose of this review is to summarize current approaches to treating pediatric classical HL according to the COG and EuroNet-PHL.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.910
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.047
GPT teacher head0.354
Teacher spread0.307 · 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 designNot applicable
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

Citations29
Published2021
Admission routes1
Has abstractyes

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