Elevated Bone Turnover Predicts for Bone Metastasis in Postmenopausal Breast Cancer: Results of NCIC CTG MA.14
Bibliographic record
Abstract
PURPOSE: We investigated the association of bone-only relapse with a pretreatment marker of bone resorption: serum beta C-terminal telopeptide (B-CTx) of type I collagen. METHODS: Pretreatment serum B-CTx concentrations were determined from 621 of 667 patients with primary breast cancer enrolled onto the NCIC CTG MA.14 phase III adjuvant trial of tamoxifen with or without octreotide. Recurrence-free survival (RFS) was a secondary end point; the focus here was bone-only relapse. We analyzed continuous or categorical (.71 ng/mL cut point) serum B-CTx in stepwise forward multivariate Cox regression, adjusted for trial stratification factors. We also examined B-CTx and bone relapse by pretrial chemotherapy status. RESULTS: At median 7.9 years follow-up, 123 of 621 patients experienced recurrence; 19 (3.1%) of 621 had bone-only recurrence, and 47 (7.5%) of 621 had bone plus other sites of recurrence. Larger pathologic tumor size (P = .001) and elevated continuous and categorical serum B-CTx were associated with shorter bone-only RFS (both P = .02) when added to a model with factors significant in the main trial analyses (hazard ratio [HR], 3.43 and 3.50, respectively; 95% CI, 1.20 to 9.77 and 1.26 to 9.75, respectively). The univariate HR for B-CTx was 2.80 (95% CI, 1.05 to 7.48; P = .03). Elevated serum B-CTx was also associated with shorter bone-only RFS (P = .02) when added to a model with factors significant in the main trial analyses. Serum B-CTx level was not associated with any other type of recurrence. Serum B-CTx was not significantly different for patients who underwent pretrial chemotherapy, compared with those who did not (P = .27), nor did pretrial chemotherapy affect bone relapse (P = .48 for bone only; P = .76 for bone with other relapse). CONCLUSION: Higher pretreatment serum B-CTx was a significant predictor of shorter RFS for bone-only metastasis. Increased bone resorption creates an environment that promotes growth of breast cancer cells.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".