Predictive value of osteopenia as prognostic marker for survival and recurrence in patients with gastrointestinal cancers: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Early detection, systematic prevention, and personalized therapy are crucial to reduce mortality in patients with gastrointestinal (GI) cancers. This systematic review and meta-analysis aimed to clarify the predictive value of osteopenia and osteosarcopenia as prognostic markers of survival and recurrence in patients with GI cancers. Methods: Medline, Google Scholar, and Science Direct databases were searched for English-language studies that included patients who underwent surgical resection following a pathologically diagnosed GI cancer and reported the association between osteopenia and osteosarcopenia on the overall survival (OS) and recurrence-free survival (RFS). Meta-analysis was done using STATA 14.2, and the results were reported as pooled hazard ratios (HR) with 95% confidence intervals (CI). Heterogeneity was assessed using the I2 statistic and the Chi-square test. Study quality was evaluated using the Newcastle Ottawa Scale (NOS). Results: A comprehensive literature search yielded 23 eligible studies, primarily from Japan. Osteopenia emerged as a significant risk factor for both OS (pooled HR 2.20, 95% CI: 1.74-2.79) and RFS (pooled HR 2.15, 95% CI: 1.60-2.89). Patients with osteosarcopenia exhibited threefold higher mortality rates (pooled HR 2.96, 95% CI: 1.99-4.40) and heightened risk of recurrence (pooled HR 2.75, 95% CI: 1.79-4.24). Subgroup analyses underscored the consistency of these associations across diverse contexts. Conclusion: This meta-analysis establishes osteopenia and osteosarcopenia as robust prognostic indicators for survival and recurrence in GI cancers. Integrating musculoskeletal assessments into routine oncological care is imperative for timely interventions and optimized patient outcomes.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.043 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".