The parathyroid hormone, its fragments and analogues - potent bone-builders for treating osteoporosis
Bibliographic record
Abstract
As populations age a rising number of men and women, but especially women during the first decade after menopause, become victims of a severe, accelerated loss of bone with crippling fractures known as osteoporosis. This often results in costly, prolonged hospitalisation and perhaps indirectly, death. Osteoporosis in women is caused by the menopausal oestrogen decline, which removes several key restraints on the generation, longevity and activity of bone-resorbing osteoclasts. Although there are many antiresorptive drugs on or coming onto the market (calcitonin, bisphosphonates, oestrogen and SERMS) that can slow or stop further bone loss, there are none that can restore lost bone mechanical strength by directly stimulating osteoblast activity and bone growth. However, there is a family of potent bone-building peptides, namely the 84 amino acid parathyroid hormone (PTH). Its 31 to 38 amino acid N-terminal fragments are currently in or about to enter clinical trials. We can predict that these peptides will be effective therapeutics for osteoporosis especially when supplemented with bisphosphonates or SERMs to protect the new bone from osteoclasts. These peptides should also accelerate the healing of fractures in persons of all ages and restore lost bone mass and mechanical strength to astronauts following their return to earth after long voyages in space.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".