Bibliographic record
Abstract
For patients with spine pain, the future holds great promise. Treatment methods of the past reflect limited insight into the spine and include techniques used to treat patients with spine disorders that now seem rudimentary. Researchers are advancing our understanding of cellular biology and the ways in which cells communicate, and more efficient and effective treatments are already changing the treatment landscape. Surgeons relied on cortico-cancellous graft without instrumentation to stimulate fusion, although in retrospect, the properties and functions were not fully understood. Bone grafting failures led to the use of stainless-steel devices that provided stability but did not participate in a biological fashion to promote healing and strength. A new frontier has emerged. Autogenous and allogenic bone graft once thought to consist of dead tissue actually may contain cells and proteins that signal events that control formation of bone, cartilage, and/or soft tissue. A new era of tissue engineering is under way, with surgeons now injecting patients with proteins and other components of cancellous bone, including bone marrow, mesenchymal stem cells (MSCs), and hematopoietic cells, to promote healing of tendons, cartilage, muscle, spinal cord, and ligaments, for example. Continued studies have led to our awareness that MSCs are pericytes that live on the capillary and are activated by trauma to move off the capillary and morph into stem cells that stimulate a healing response—resulting in a change in the definition of “MSCs” by Arnold Caplan, PhD, from “mesenchymal stem cells” to “medicinal signaling cells.” How do they talk to each other? When injected into the vein, how do stem cells move from the vasculature into the tissue? Much has been learned about orthopedic treatment at organ and cellular levels, but the next advances will occur at the molecular level—cell-to-cell signaling and cell-to-cell control. Ongoing research is examining ways to manipulate biomaterial at the nano level to make the orthopedic implant biologically active and able to participate in and enhance tissue repair and tissue engineering. We have discovered inside the stem cell the entire orchestra that we need to create and repair tissue. We have modified surfaces to make them more bioreactive. Revolutionary treatments for degenerative disease and spinal deformity and injury are at hand. We look forward to the future!
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".