A window beneath the skin: how computed tomography assessment of body composition can assist in the identification of hidden wasting conditions in oncology that profoundly impact outcomes
Bibliographic record
Abstract
Advancements in image-based technologies and body composition research over the past decade has led to increased understanding of the importance of muscle abnormalities, such as low muscle mass (sarcopenia), and more recently low muscle attenuation (MA), as important prognostic indicators of unfavourable outcomes in patients with cancer. Muscle abnormalities can be highly prevalent in patients with cancer (ranging between 10 and 90 %), depending on the cohort under investigation and diagnostic criteria used. Importantly, both low muscle mass and low MA have been associated with poorer tolerance to chemotherapy, increased risk of post-operative infectious and non-infectious complications, increased length of hospital stay and poorer survival in patients with cancer. Studies have shown that systemic antineoplastic treatment can exacerbate losses in muscle mass and MA, with reported loss of skeletal muscle between 3 and 5 % per 100 d, which are increased exponentially with progressive disease and proximity to death. At present, no effective medical intervention to improve muscle mass and MA exists. Most research to date has focused on treating muscle depletion as part of the cachexia syndrome using nutritional, exercise and pharmacological interventions; however, these single-agent therapies have not provided promising results. Rehabilitation care to modify body composition, either increasing muscle mass and/or MA should be conducted, and its respective impact on oncology outcomes explored. Although the optimal timing and treatment strategy for preventing or delaying the development of muscle abnormalities are yet to be determined, multimodal interventions initiated early in the disease trajectory appear to hold the most promise.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".