Bibliographic record
Abstract
Early detection of abnormalities in bone turnover may be facilitated by assessing biomarkers of bone metabolism including vitamin D status. In many children with cancer, biomarkers of bone formation (osteocalcin, bone specific alkaline phosphatase and carboxy-(or N terminal) propeptide of type 1 procollagen) were observed to be suppressed, while bone resorption was elevated as measured by serum cross-linked (or C-terminal) telopeptide of type 1 collagen. Insulin-like growth factor 1, which stimulates bone formation, may be suppressed indirectly indicating a growth hormone insufficiency. Leptin may also play a role in bone remodeling as hyperleptinemia has been observed in association with acute lymphoblastic leukemia. Evaluation of bone status using such biomarkers is complicated by the lack of universally accepted reference values and the variation by age, gender, or pubertal status. Etiologic factors contributing to the observed skeletal morbidities include disease process, chemotherapy (drugs such as glucocorticoids and methotrexate) and radiotherapy. Other factors common to children with cancer, such as chronic inflammation, dietary changes and physical inactivity, must also be taken into account. The current evidence for abnormalities in biomarkers of vitamin D status and bone turnover will be the focus of this review of published 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".