Exploring decisional needs of adults considering first line treatment for advanced EGFR+ lung cancer: An interpretive descriptive study
Bibliographic record
Abstract
BACKGROUND: With expanding treatment options for EGFRs+ metastatic non-small cell lung cancer (mNSCLC), shared decision-making is critical in aligning treatment plans with patient values. Oral targeted therapies offer convenience and independence and emerging therapies combining targeted therapies with various IV therapies show improved efficacy but with increased side effects and impacts on quality of life. This study explores the decisional needs of adults with EGFR+ mNSCLC considering first-line treatments. METHODS: We conducted an interpretive descriptive qualitative study guided by the Ottawa Decision Support Framework. INCLUSION CRITERIA: adults mEGFR+ NSCLC with current or prior Osimertinib therapy. Interviews were conducted using a standardized interview guide. Interview transcripts were analyzed using inductive thematic analysis and mapped onto the Framework. RESULTS: The 16 participants were aged 48-83 years (median 62); 11 female; 13 currently taking Osimertinib; and time since diagnosis was 2 to 78 months. Decisional needs include inadequate discussion on alternatives; decisions under pressure; feeling overloaded with written information; desire for value-aligned treatment; deferring to trusting relationships; strong emotions interfering with decision-making; desire for structured decisional supports. Participants valued Osimertinib's convenient oral delivery, minimal side effects, and ability to maintain independence. When asked about newer combined treatments, participants wanted more details so they could weigh the options to determine the best fit for their personal circumstances. CONCLUSION: Our findings emphasize the need for oncologists to recognize patients' decisional needs in treatment discussions and consider ways to support them to ensure treatment choices align with patients' informed values.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".