Osteogenesis imperfecta, current and future medical treatment
Bibliographic record
Abstract
Physiotherapy, rehabilitation, and orthopedic surgery are the mainstay of treatment in moderate to severe forms of osteogenesis imperfecta (OI). Nevertheless, medical treatment with bisphosphonates can bring significant additional improvements. Benefits include decreased pain, lower fracture incidence, and better mobility. Among the various bisphosphonates, intravenous pamidronate has been studied in most detail. It is unclear whether oral bisphosphonates are as effective as intravenous pamidronate. As the effect of bisphosphonates on the skeleton is largest during growth, it appears logical to start medical therapy of OI patients as early as possible. However, the optimal treatment regimen and the long-term consequences of pamidronate treatment in children are currently unknown. Given these uncertainties, treatment with bisphosphonates during growth should be reserved for patients who have significant clinical problems, such as vertebral compression fractures or long bone deformities. Medical therapies other than bisphosphonates, such as growth hormone and parathyroid hormone, play a minor role at present. Gene-based therapy currently remains in the early stages of preclinical research.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".