Suppression of bone turnover markers by zoledronic acid and correlation with clinical outcome
Bibliographic record
Abstract
532 The bone resorption marker N-telopeptide (Ntx) provides valuable prognostic information in patients with bone metastases. Across all tumor types an elevated baseline Ntx increases the relative risk of skeletal-related events (SREs), disease progression and death (Brown, et al., ASCO 2003). In this study, urinary N-telopeptide was measured at baseline and at month 3 in 290 patients with breast cancer and multiple myeloma treated with monthly 4 mg of zoledronic acid. At baseline, 120 patients had a urinary Ntx value of 0–64 nmol/mmol creatinine (upper limit of normal for premenopausal women) and 170 had an elevated baseline Ntx (greater than 64 nmol/mmol creatinine). At month 3 of zoledronic acid treatment, only 2/120 (1.7%) patients who started with normal Ntx developed an elevated value. At month 3, 137/170 (80.6%) of patients who began treatment with elevated Ntx had a normal Ntx, 26/170 (15.3%) had persistent elevation of Ntx, and 7/170 (4.1%) patients died. A persistently elevated Ntx (greater than 64 nmol/mmol creatinine) was a significant predictor of subsequently developing a first SRE (RR = 1.71; p = 0.035) and SRE-free survival (RR = 1.65; p = 0.039). It did not predict time to progression of cancer in the skeleton (RR = 1.26; p = 0.417) or survival (RR = 1.33, p = 0.316). In summary, in breast cancer and multiple myeloma patients, normalization of an elevated baseline urinary Ntx at month 3 of treatment with zoledronic acid is a significant predictor of favorable outcome as measured by SREs and time to first SRE. Author Disclosure Employment or Leadership Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Novartis Novartis Novartis Novartis Novartis Novartis Novartis
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".