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Record W4411321377 · doi:10.1002/hon.70096_549

549 | PROSPECTIVE PATIENT PREFERENCE STUDY FOR CHRONIC LYMPHOCYTIC LEUKEMIA TREATMENT ATTRIBUTES IMPACTING PATIENT SHARED‐DECISION MAKING

2025· article· en· W4411321377 on OpenAlexaff
Sikander Ailawadhi, Swetha Challagulla, Dominic Pilon, Todor Totev, Yan Meng, Lilián Díaz, Zhiguo Chen, Kehu Yang

Bibliographic record

VenueHematological Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsGroup for Research in Decision Analysis
FundersBeiGene
KeywordsChronic lymphocytic leukemiaPreferenceMedicineLeukemiaOncologyIntensive care medicineInternal medicineStatistics

Abstract

fetched live from OpenAlex

Introduction: CLL is the most common leukemia in adults in Western countries, incidence 5/100,000 in US/Europe, and significantly lower in Asia (0.48).Latin America (LATAM) presents a complex and variable landscape for CLL incidence, with some countries like Uruguay and Argentina exhibiting rates similar to Europe, while others (Mexico, Peru, Chile) reporting lower incidences.The region's ethnic, cultural, and economic heterogeneity leads to disparities in access to diagnostic and prognostic tools, as well as therapeutic options, especially with the increasing use of targeted agents.In 2022, the GELL-CLL cohort presented initial data from 459 patients across six countries, here, we provide an updated analysis, expanding the cohort.Methodology: retrospective cohort study of CLL patients aged ≥ 18 years, diagnosed and treated between 2010 and 2024, or diagnosed since 2000 and treated from 2010 onwards, from centers participating in the GELL-CLL registry.Results: 981 patients from ten countries (Argentina, Chile, Colombia, Cuba, Guatemala, Mexico, Paraguay, Peru, Uruguay, Venezuela) were included, with 883 eligible.Of these, 66% were treated in private institutions.Median age 68 years (30-95), 40.4% female.Racial distribution: 89.8% White, 1.5% Africanancestry, 1.2% Indigenous, 7.2% mixed-race, and 0.1% Asian.At diagnosis, 91.4% ECOG 0-1, 46% Rai 0; III-IV: 14.2%.Key prognostic factors revealed that 13.8% were CD38þ, and elevated B2 was found in 28%.IgVH mutational status was studied in 29.6% of patients, with 47% unmutated.At diagnosis, 77% of patients were under observation, with 51% requiring treatment after a median of 9 months.Of those treated, only 33% underwent cytogenetic or FISH analysis prior to therapy.Del17p was identified in 3.7% of patients, while P53 mutations were found in 1.5%.First-line treatment included chemo-immunotherapy (55.2%), chemotherapy (28.4%), iBTK (12.3%), and Venetoclax-based (4.1%).Remarkably, 70% of patients receiving chemotherapy had no prior cytogenetic or FISH testing.Differences in biomarker testing between countries were stark, ranging from 0% in Venezuela to 45% in Uruguay.Median follow-up 67 months (0-362), overall survival (OS) differed significantly by treatment era: OS was 116 months for 2010-2014, 127 months for 2015-2019, and was not reached for 2020-2024 (p = 0.015).Four-year OS rates were 65% for chemotherapy, 81% for chemo-immunotherapy, 84% for iBTK, and 90% for Venetoclax-based.Conclusions: This real-world analysis highlights significant disparities in CLL management across LATAM.Despite recent shifts toward targeted therapies, many patients still lack access to essential prognostic testing, potentially leading to suboptimal treatment choices.Efforts to improve biomarker availability and targeted therapy access are crucial for enhancing outcomes across the region.Expanding this cohort will further elucidate regional variations and support initiatives to address treatment inequities.

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)0.007
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.071
GPT teacher head0.399
Teacher spread0.328 · 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 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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