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Exploring decisional needs of patients considering first line treatment of advanced EGFR+ lung cancer: An interpretive descriptive study.

2025· article· en· W4410803224 on OpenAlexaffabout
Rena Seeger, Dawn Stacey, Emi Bossio, Paul Wheatley‐Price

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineLung cancerDescriptive researchOncologyDescriptive statisticsInternal medicine

Abstract

fetched live from OpenAlex

8612 Background: With expanding treatment options for EGFR+ metastatic non-small cell lung cancer (mNSCLC), shared decision-making is important in aligning treatment plans with patient values. Targeted therapies like osimertinib offer convenience and independence. New studies like FLAURA2 and MARIPOSA explore therapeutic combinations with intravenous drugs, demonstrating potentially superior efficacy but more side effects, highlighting the need for unbiased and patient-centered approaches. This study explores the decisional needs of patients considering first-line treatments. Methods: We conducted an interpretive descriptive qualitative study guided by the Ottawa Decision Support Framework to explore the experiences and perspectives of EGFR+ mNSCLC patients. Interviews were conducted via Microsoft Teams. Inclusion criteria: 18+ years; mEGFR+ NSCLC; current/prior osimertinib therapy; proficient in English. Interviews were conducted using a standardized interview guide with inductive thematic analysis. A sample size of 10-12 was considered sufficient to saturate ideas from participant responses, with additional 3 recruited to ensure saturation. Themes were mapped onto the Ottawa Decision Support Framework. Results: Sixteen participants were interviewed from Sep-Nov 2024: age 48-83 (median 62 years); 11 female; 13 currently taking osimertinib; 10 diagnosed >1 year. Many patients reported relying on oncologist recommendation without participation in decision making. Patients with young children had an increased desire to be actively involved in treatment decisions. Key themes from preliminary analysis identified: patients overwhelmingly trusted their oncologist , felt pressure to start treatment quickly , were overwhelmed with the diagnosis , had inadequate knowledge of the treatment and potential side effects, and felt highly responsible to ensure proper drug administration . Patients valued e ase and convenience of [osimertinib] treatment, fe w severe side effects, being alive, continuing day to day living , and remaining independent for family and travel. When asked about combination therapy, patients valued quality of life , avoiding increased hospital trips and side effects , but indicated a willingness to try . Conclusions: Key themes from preliminary analysis identify crucial components at initial diagnosis, with patients feeling overwhelmed and having inadequate knowledge, thus relying on their trusted oncologists’ recommendation. While patients highly valued independence, fewer visits and quality of life, they were willing to try combination therapy, highlighting the importance of oncologists understanding their patient’s individual needs and goals of treatment. Treatment choices should reflect the patient’s values. These results will be used to create a decision aid that we can pilot in our oncology clinics.

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)0.018
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.008
Scholarly communication0.0060.007
Open science0.0020.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.184
GPT teacher head0.524
Teacher spread0.340 · 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 designQualitative
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

Citations0
Published2025
Admission routes2
Has abstractyes

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