Symptoms and Experiences with Small Cell Lung Cancer: A Mixed Methods Study of Patients and Caregivers
Bibliographic record
Abstract
INTRODUCTION: Understanding of the patient-perceived symptom burden of small cell lung cancer (SCLC) is limited. The objective of this study was to explore patients' experiences with SCLC, identify which treatment-/disease-related symptoms have the greatest impact on their well-being, and gain caregiver perspectives. METHODS: A noninterventional, cross-sectional, multimodal, mixed methods study was conducted from April-June 2021. Adult patients with SCLC and unpaid caregivers were eligible to participate. Patients' experiences, captured via 5-day video diaries and follow-up interviews, were scored 1-10 on how bothersome the patients perceived each symptom/symptomatic adverse event. Patients indicated if they believed a symptom was disease or treatment related. Caregivers participated in an online community board. RESULTS: The study included nine patients (five with extensive-stage [ES] disease, four with limited-stage [LS] disease) and nine caregivers. Except for one patient/caregiver pairing, patients and caregivers were unmatched. The most common impactful symptoms in patients with ES-SCLC were shortness of breath, fatigue, coughing, chest pain, and nausea/vomiting; in LS-SCLC, these were fatigue and shortness of breath. Among patients with ES disease, SCLC had a high impact on physical (leisure/hobbies, work, sleep, ability to do household chores and errands/responsibilities outside home), social (family dynamics, extrafamilial social interaction), and emotional (mental health) aspects. Patients with LS-SCLC faced the long-term physical effects of treatment, financial implications, and emotional toll of an uncertain prognosis. SCLC had a high personal and psychologic burden among caregivers, whose duties consumed much of their time. Caregivers observed similar symptoms and impacts of SCLC as those reported by patients. CONCLUSIONS: This study provides valuable insight into patient- and caregiver-perceived burden of SCLC and can inform the design of prospective studies. Clinicians should seek to understand patients' opinions and priorities before making treatment decisions.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".