Bone alkaline phosphatase as a surrogate marker of bone metastasis in gastric cancer patients
Bibliographic record
Abstract
BACKGROUND: Bone metastasis is relatively uncommon in gastric cancer patients, but its incidence has been rising. Early detection of bone metastasis is important in preventing complications related to bone metastasis such as pain, fracture and the compromise of chemotherapy. In this pilot study, we investigated the feasibility of bone turnover markers as surrogate markers of bone metastasis in gastric cancer patients. METHODS: Fifty-eight patients with gastric cancer were included in this study. Serum levels of bone alkaline phosphatase (ALP), parathyroid hormone (PTH), 25(OH) D, osteocalcin (OC) and C terminal telopeptide were measured and compared between patients with bone metastasis and those without. Student's t-test and Mann-Whitney U test were used in comparing two groups, and Spearman's rank order correlation coefficient was calculated to quantify the strength of the associations. RESULTS: Fifty eight age- and sex-matched patients were evaluated for bone turnover markers, among whom 29 patients had bone metastasis and 29 patients with no bone metastasis. The median age was 62 and there were 20 (68.9 %) males and 9 (31.1 %) females in each group. Bone ALP was significantly higher in the patient group (57.32 ± 46.83 vs. 34.57 ± 21.57, P = 0.037) than control group. Bone ALP was positively associated with ALP, osteocalcin, CA19-9, CA 72-4 and negatively associated with 25(OH) D. According to ROC-curve analysis, at the threshold value of 29.60 μg/L, the sensitivity of bone ALP was 76.7 % and the specificity was 59.4 %. CONCLUSION: Bone ALP may be a surrogate marker of bone metastasis in gastric cancer patients. More prospective studies are warranted to determine the optimal bone turnover markers in the evaluation of bone metastasis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".