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Record W4412178530 · doi:10.3324/haematol.2025.287772

Improving chronic myeloid leukemia management and quality of life: patient and physician survey on unmet needs from the CML SUN survey

2025· article· en· W4412178530 on OpenAlexaff
Fabian Lang, Zack Pemberton‐Whiteley, Joannie Clements, Cristina Ruiz, Delphine Réa, Lisa Machado, Naoto Takahashi, Andrew Grigg, Cornelia Borowczak, Peter Schuld, Pauline Frank, Cristina Constantinescu, Carla Boquimpani, Jörge E. Cortes

Bibliographic record

VenueHaematologica · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsCanadian Respiratory Research Network
Fundersnot available
KeywordsMedicineTolerabilityFamily medicineQuality of life (healthcare)DiseaseMyeloid leukemiaInternal medicineNursingAdverse effect

Abstract

fetched live from OpenAlex

For patients with chronic myeloid leukemia in chronic phase (CML-CP), disease management, treatment experiences, and decisions around switching therapies due to resistance or intolerance can have significant impacts on their lives. Experiences and perspectives regarding the roles of patients and treating physicians in shared decision-making are poorly understood. The CML Survey on Unmet Needs (CML SUN), the largest CML survey to date, was initiated to gather insights from patients with CML-CP and physicians on disease management, including treatment goals, decision-making, satisfaction, tolerability, and the impact of CML on daily life. The survey was deployed in 11 countries with 361 patient and 198 physician participants and comprised separate questionnaires for each group. Results indicated that nearly three-quarters of physicians saw themselves as the ultimate initial treatment decision-makers; only a quarter of patients reported that these decisions were discussed and decided together with their physician. Nearly half of physicians reported making treatment decisions across all lines of therapy with little to no input from the patient. Disparities between patient and physician opinions were observed regarding treatment goals, especially the balance between efficacy and tolerability. The CML SUN highlights the need for improvements in communication about treatment options and the importance of shared treatment decision-making to unify treatment goals.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.049
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.037
GPT teacher head0.285
Teacher spread0.248 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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