Bridging the gap: Compressing non-unions for proper cellular signaling
Bibliographic record
Abstract
Non-unions of fractured bones are long-lasting painful conditions with a large socio-economic burden. Surgical intervention is the only treatment option, with firm compression of the bone fragments often resulting in non-union healing. Why compression works as a treatment for non-unions, however, remains poorly understood, because static loading is generally considered as non-osteogenic. In recent years, the crucial role of osteocytes and specific molecular pathways like the RankL/OPG, BMP and Sclerostin/Dkk1/Wnt axes have been identified as critical for bone healing. Furthermore, the role of mechanical loading and osteocyte deformation leading to a decrease or complete blocking of Sclerostin secretion – which in turn leads to activation of the osteogenic Wnt signaling pathway, has been elucidated. Our hypothesis states that osteocytes are the primary mediators of non-union healing. Therefore, in order to switch the resting osteocytes towards an osteogenic mode, firm compression of bone fragments is required for successful treatment of non-unions. This compression is necessary in order to restore the mechanical continuity of the bone, so that activities of daily life can physically stimulate the osteocytes, which in turn direct the bone healing process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".