Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) have firmly occupied the attention of orthopedic clinicians and scientists for most of the last 25 years. Hundreds of laboratories worldwide have carried out research aimed at unraveling the biological characteristics of these cells and probing the manner in which they potentially contribute to cartilage and bone repair. Clinical trials registries indicate that they are also being tested in patient studies for a wide range of conditions such as osteoarthritis, rheumatoid arthritis, fracture repair, regeneration of articular cartilage, tendon repair, and for treatment of degenerative disc disease. Despite these efforts, the effectiveness of MSCs as a treatment modality for these conditions is still uncertain and market authorizations have been limited. In addition, critical and clear phenotypic parameters for defining MSCs are uncertain and a coherent biological framework surrounding the therapeutic mechanism of action is not yet available. Added to this, cell manufacturing protocols are complex and costly and present substantial challenges in terms of regulatory oversight and standardization. Despite these obstacles, MSCs still remain at the forefront of efforts in Regenerative Medicine, based on a conviction that this technology can provide an effective treatment paradigm for major diseases where there is still an unmet need. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1229-1235, 2019.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".