Bone Bioelectricity and Bone-Cell Response to Electrical Stimulation: A Review
Bibliographic record
Abstract
It is hypothesized that bone cells can sense mechanical force in the extracellular network via an electrical signal. This has led to the use of electrical stimulation (ES) to improve fracture repair and mitigate bone loss. Although overlap exists in bone maintenance and fracture healing mechanics, the processes involved in both are very different, resulting in dissimilar behaviors from the cells. Osteocytes are the most abundant cell type in bone tissue, and their basic structure and lineage are fairly well understood, but much debate is present regarding their behavior, with even less known about their behavior in electrical environments. A wide range of research exists on cell behavior under different types of ES, but it is difficult to draw conclusions due to the large variance in stimulation parameters, cell types, and origins (locations and species). By exploring behavior of multiple bone-cell types under different forms of ES, as well as mechanical stimulation through fluid flow, we can determine more about cell reactions to stimuli. In turn, a better understanding of cell response has the potential to improve and broaden therapeutic applications of ES for bone healing and bone loss mitigation, and enhance outcomes for osseointegration into implantable medical devices. These require greater understanding of the bone cellular environment from an electrical perspective as well as cellular responses to ES.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".