A novel oral hPTH(1-34) unveils the promise of modeling-based anabolism with no increase in bone remodeling
Bibliographic record
Abstract
Bone turnover markers (BTMs) have emerged as promising tools in the management of osteoporosis as they provide dynamic information regarding skeletal status that is independent from, and often complementary to, BMD measurements. Despite remaining issues with reference intervals and assays harmonization, BTMs have been useful in elucidating the pharmacodynamics and effectiveness of osteoporosis medications in clinical trials.1 Serum procollagen type I N-propeptide (sPINP) and serum C-telopeptide cross-linked type I collagen (sCTX) have been identified as the most promising markers of bone formation and resorption, respectively, in osteoporosis. Among individuals not receiving osteoporosis treatment, resorption and formation rates are tightly linked and positively highly correlated (r = 0.6-0.8).1 Early changes (3 mo) in sPINP correlate with the percentage change in LS BMD after 18 mo of treatment with both abaloparatide and teriparatide, although the correlation with abaloparatide was greater.2 Although absolute levels of sPINP and sCTX were lower with abaloparatide than teriparatide, suggesting a lower turnover, the balance (uncoupling index) between the formation and resorption marker was similar.2
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".