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Record W4319159835 · doi:10.2147/ppa.s394332

A Survey of Patient Experience in CML: American and Canadian Perspectives

2023· article· en· W4319159835 on OpenAlexaffabout
Christopher Hillis, Kathryn E. Flynn, Erinn Hoag Goldman, Tracy Moreira‐Lucas, Josie Visentini, Stephanie Dorman, Rachel Ballinger, Hilary F. Byrnes, Andrea De Palma, Valentin Barbier, Lisa Machado, Ehab Atallah

Bibliographic record

VenuePatient Preference and Adherence · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsPfizer (Canada)McMaster University
FundersPfizer
KeywordsMedicineQuality of life (healthcare)Family medicinePatient satisfactionMyeloid leukemiaInternal medicineNursing

Abstract

fetched live from OpenAlex

Purpose: With treatment, chronic myeloid leukemia (CML) has a favorable prognosis, however, individuals with CML experience impairment to their quality of life (QoL). The aim of this study was to examine the perspectives and experiences of individuals with CML and to understand their challenges communicating with their CML physician. Patients and Methods: An online survey in adults with CML (n=100) in the US and Canada assessed QoL, patient-provider relationships, treatment satisfaction, and understanding of CML and treatment goals via the MD Anderson Symptom Inventory, the Cancer Therapy Satisfaction Questionnaire and de novo survey questions. Participants were recruited via an external patient recruiter and CML Patient Groups. Results: Many participants reported hardships due to CML and its treatment. The main impacts were on the ability to work (21%), engage in personal activities (e.g., hobbies, 28%), and to enjoy sexual relations (median=2.00, IQR=8.50). A substantial proportion (21-39%) wished to discuss additional topics with their providers (e.g., management of CML and/or its impacts). While participants reported satisfaction with therapy overall (median=85.71, IQR=17.86), they indicated low to moderate treatment satisfaction with specific components, including concerns regarding side effects (median=43.75, IQR=43.75). Participants generally had a good understanding of CML (97%) and its treatment goals (92%). Conclusion: These findings advance our understanding of issues that need improvement to support QoL for individuals living with CML. Future work is needed to improve patient-provider relationships, address treatment-related side effects, and provide clinical information that is easier for patients to understand.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.070
GPT teacher head0.307
Teacher spread0.237 · 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
Published2023
Admission routes2
Has abstractyes

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