Bibliographic record
Abstract
Introduction To attempt to understand a disease such as osteoporosis, we must come to a complete elucidation of the nature or pathology of the disorder, the cellular mechanisms whereby the pathology develops and, lastly, what caused the cellular machinery to go awry in the first place (e.g., control by genes, hormones, growth factors, vitamins, minerals, etc.). While many investigators may still disagree on a unified definition of osteoporosis which fully describes its pathology, for a number of years now we have fairly well understood the nature of the disease, and to describe the gross cellular mechanisms which do go awry. That is to say that we can all agree that the end result of undermineralized bone is due to a chronic imbalance of skeletal turnover whereby more mineral is removed than is incorporated into the matrix. It is only recently, however, that we have been able to tackle why the cellular machinery goes wrong, and that has resulted from a clearer understanding of the role(s) of growth factors and cytokines in the skeletal microenvironment. Both bone itself and the bone marrow compartment produce, store, and are influenced by a plethora of cytokines, stem factors and growth factors. However, in this brief chapter we will concentrate on those factors which are known to be both produced and stored within the matrix of bone itself; these are the insulin-like growth factors (IGFs), fibroblast growth factors (FGFs), transforming growth factor-β (TGF-β), and the bone morphogenic proteins (BMPs).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".