AB150. SOH25_AB_343. Osteosarcopenia as a prognostic indicator in advanced cancers with bony metastases: a systematic review
Bibliographic record
Abstract
Background: Osteosarcopenia, the concurrent loss of bone and muscle mass, significantly impacts cancer patients, particularly those with advanced disease. This condition exacerbates frailty, impairs treatment tolerance, and reduces survival outcomes. Despite its clinical importance, data on osteosarcopenia across cancer types, stages, and treatments remain fragmented. This study examines the association of osteosarcopenia with cancer staging, bone activity, performance status, interventions, survival outcomes, and multimodal imaging. Methods: A systematic review was completed using both Medline and Embase electronic databases. Studies that described osteosarcopenia and its relation to advanced cancers, survival, computed tomography (CT) imaging, and metastasis were included. Studies without CT imaging or survival outcomes were excluded. Primary and secondary outcomes included overall survival and response to either surgical or chemotherapeutic interventions. Results: Osteosarcopenia was often accompanied by hypoalbuminemia, elevated inflammatory markers, and reductions in functional biomarkers like grip strength which complemented CT imaging findings. Patients with a low skeletal muscle index at baseline were more likely to experience significant declines in muscle mass during treatment, worsening prognosis across cancer types. Muscle loss ≥5% during chemotherapy was strongly associated with reduced overall and progression-free survival. Overall, these patients had poorer outcomes than those with isolated sarcopenia or osteoporosis, suggesting a compounded negative effect. Conclusions: Osteosarcopenia is prevalent in advanced cancers, particularly with osteolytic activity, and correlates with poor survival. Through imaging and complementary biomarkers, osteosarcopenia was demonstrated to be a critical predictor of surgical and chemotherapeutic tolerance, survival, and recovery. This underscores the need for early intervention and tailored treatment approaches. For high-risk patients, incorporating nutritional and physical therapy strategies into cancer care pathways may mitigate muscle loss and improve overall outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".