Living with low muscle mass and its impact throughout curative treatment for lung cancer: A qualitative study
Bibliographic record
Abstract
OBJECTIVES: To 1) explore the experience of patients with lung cancer with low muscle mass or muscle loss during treatment and the ability to cope with treatment, complete self-care, and 2) their receptiveness and preferences for nutrition and exercise interventions to halt or treat low muscle mass/muscle loss. METHODS: This was a qualitative study using individual semi-structured interviews conducted using purposive sampling in adults with a diagnosis of non-small cell lung cancer (NSCLC) or small-cell lung cancer (SCLC), treated with curative intent chemo-radiotherapy or radiotherapy. Patients who presented with computed tomography-assessed low muscle mass at treatment commencement or experienced loss of muscle mass throughout treatment were included. Data were analysed using thematic analysis. RESULTS: Eighteen adults (mean age 73 ± SD years, 61% male) with NSCLC (76%) treated with chemo-radiotherapy (76%) were included. Three themes were identified: 1) the effect of cancer and its treatment; 2) engaging in self-management; and 3) impact and influence of extrinsic factors. Although experiences varied, substantial impact on day-to-day functioning, eating, and ability to be physically active was reported. Patients were aware of the overall importance of nutrition and exercise and engaged in self-initiated or health professional supported self-management strategies. Early provision of nutrition and exercise advice, guidance from health professionals, and support from family and friends were valued, albeit with a need for consideration of individual circumstances. CONCLUSION: Adults with NSCLC with or experiencing muscle loss described a diverse range of experiences regarding treatment. The types of support required were highly individual, highlighting the crucial role of personalised assessment of needs and subsequent intervention.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".