MétaCan
Menu
Back to cohort
Record W4403622122 · doi:10.58931/cht.2024.3146

Front-line Management of Follicular Lymphoma

2024· article· en· W4403622122 on OpenAlexaffabout
Samantha Hershenfeld, Jennifer Teichman, Neil L. Berinstein

Bibliographic record

VenueCanadian Hematology Today · 2024
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsFront lineFollicular lymphomaLine (geometry)Front (military)LymphomaMedicineComputer scienceGeologyInternal medicineHistoryMathematicsOceanographyGeometry

Abstract

fetched live from OpenAlex

Follicular lymphoma (FL) is the second most common type of non-Hodgkin lymphoma (NHL) in Western countries. Most patients have an indolent disease course with 10-year survival estimates of 80% among all patients in the rituximab era. However, risk stratification schema can identify subgroups of patients at higher risk of early death and/or progression following front-line therapy. In addition, histologic transformation to an aggressive NHL occurs in approximately 2% of patients per year. Many patients can initially be observed, but ultimately, most will be treated with multiple lines of therapy during their lifetimes. Current Health Canada-approved systemic treatment options include chemoimmunotherapy and lenalidomide plus rituximab. Phosphoinositide 3-kinase (PI3K) inhibitors were initially approved but were later withdrawn because of toxicity considerations. Newer therapies likely to impact care in Canada include bispecific T cell engagers (BiTEs) and chimeric antigen receptor (CAR)-T cell therapy.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.013
GPT teacher head0.258
Teacher spread0.246 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Hematology TodaySame topicLymphoma Diagnosis and TreatmentFrench-language works237,207