Magnesium Depletion Score as a Prognostic Indicator in Endometrial Cancer: A Retrospective Cohort Study
Bibliographic record
Abstract
Magnesium is essential for cellular metabolism, and its deficiency has been associated with adverse outcomes in various cancers. The MDS, which considers factors such as diuretic and proton pump inhibitor use, alcohol consumption, and kidney function, is a practical indicator of Mg deficiency. This retrospective cohort study assessed 200 patients with EC treated between 2010 and 2024 to explore the prognostic value of MDS. Patients were divided into low (0–1), intermediate (2), and high (≥3) MDS risk categories. Higher MDSs were significantly associated with older age, comorbid conditions, hypertension, diabetes, and reduced serum magnesium and vitamin D levels (all p < 0.001). Kaplan–Meier analysis revealed that patients with high MDSs experienced notably shorter overall and progression-free survival than those with lower scores. Multivariate Cox regression analysis identified age, tumor grade, lymphovascular invasion, and stage as independent prognostic factors, excluding those for MDS. These results indicate that although MDS is associated with comorbidities, biochemical deficiencies, and poorer unadjusted survival, it does not independently predict the prognosis of EC. The MDS could be a straightforward and cost-effective tool for identifying metabolically vulnerable patients, especially among the elderly, and merits further validation in prospective studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".