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Record W3201943516 · doi:10.3390/curroncol28050326

Frontline Treatment of the Young, Fit Patient with CLL: A Canadian Perspective

2021· review· en· W3201943516 on OpenAlexafffundvenueabout
Jacqueline Costello, Matthew Kang, Versha Banerji

Bibliographic record

VenueCurrent Oncology · 2021
Typereview
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsSt. John’s Health Sciences CentreCancerCare ManitobaMcMaster UniversityUniversity of ManitobaJoseph Brant HospitalMemorial University of Newfoundland
FundersJanssen Canada
KeywordsIbrutinibMedicineFludarabineRituximabChemoimmunotherapyPopulationChronic lymphocytic leukemiaInternal medicineOncologyCyclophosphamideIntensive care medicineLeukemiaChemotherapyLymphomaEnvironmental health

Abstract

fetched live from OpenAlex

From a Canadian perspective, there has been a limited discussion on the frontline management of young, fit patients with chronic lymphocytic leukemia (CLL). The prevalence of this population ranges between 2 and 22 per 100,000 persons in Canada and varies by region. Until recently, fixed-duration fludarabine-based chemoimmunotherapy (CIT) was the primary treatment option in Canada for this patient population. The ECOG1912 trial has since demonstrated that ibrutinib and rituximab therapy are as effective as fludarabine-cyclophosphamide-rituximab (FCR) in this population. The ALLIANCE trial showed that rituximab added no incremental benefit to ibrutinib. Canadian payors and physicians adopted ibrutinib monotherapy as the CLL standard of care, even in the young, fit population, although frontline ibrutinib therapy is often reimbursed by provincial public drug plans only in patients with high-risk disease or those who are unfit to receive fludarabine. Young, fit patients with CLL and their physicians may now choose between continuous ibrutinib monotherapy and fixed-duration CIT with FCR. Factors affecting this choice include patient preference and the short- and long-term toxicity profiles of both regimens, and a risk-based algorithm is provided. As new continuous-therapy options enter the market, all treatment choices present benefits and risks that must be communicated to the patient.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.215
GPT teacher head0.483
Teacher spread0.268 · 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 designOther design
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

Citations3
Published2021
Admission routes4
Has abstractyes

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