Predicting muscle loss during lung cancer treatment (PREDICT): protocol for a mixed methods prospective study
Bibliographic record
Abstract
INTRODUCTION: Low muscle mass and low muscle attenuation (radiodensity), reflecting increased muscle adiposity, are prevalent muscle abnormalities in people with lung cancer receiving curative intent chemoradiation therapy (CRT) or radiation therapy (RT). Currently, there is a limited understanding of the magnitude, determinants and clinical significance of these muscle abnormalities in the lung cancer CRT/RT population. The primary objective of this study is to identify the predictors of muscle abnormalities (low muscle mass and muscle attenuation) and their depletion over time in people with lung cancer receiving CRT/RT. Secondary objectives are to assess the magnitude of change in these parameters and their association with health-related quality of life, treatment completion, toxicities and survival. METHODS AND ANALYSIS: Patients diagnosed with lung cancer and planned for treatment with CRT/RT are invited to participate in this prospective observational study, with a target of 120 participants. The impact and predictors of muscle abnormalities (assessed via CT at the third lumbar vertebra) prior to and 2 months post CRT/RT on the severity of treatment toxicities, treatment completion and survival will be assessed by examining the following variables: demographic and clinical factors, weight loss, malnutrition, muscle strength, physical performance, energy and protein intake, physical activity and sedentary time, risk of sarcopenia (Strength, Assistance in walking, Rise from a chair, Climb stairs, Falls history (SARC-F) score alone and with calf-circumference) and systemic inflammation. A sample of purposively selected participants with muscle abnormalities will be invited to take part in semistructured interviews to understand their ability to cope with treatment and explore preference for treatment strategies focused on nutrition and exercise. ETHICS AND DISSEMINATION: The PREDICT study received ethics approval from the Human Research Ethics Committee at Peter MacCallum Cancer Centre (HREC/53147/PMCC-2019) and Deakin University (2019-320). Findings will be disseminated through peer review publications and conference presentations.
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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 | 0.035 | 0.026 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.010 |
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".