Developing Stem Cell-Based Therapeutic Strategies in Orthopaedic Surgery
Bibliographic record
Abstract
The term "stem sell" first appeared in scientific literature in 1868, when the German scientist Ernst Haeckel merged the concepts of phylogeny and ontogeny to describe the "stammzelle" (stem cell), an evolutionary concept of a primordial cell that evolves into all cells and multicellular organisms Although Haeckel's argument and use of the term "stem cell" nearly 150 years ago were impressive, they were based on his observations of embryo development and the distinction between fertilized and nonfertilized eggs. The first definitive evidence for the existence of stem cells came in the early 1960s, after the Canadian scientists Ernest McCulloch and James Till performed experiments on the bone marrow of mice and observed that different blood cells come from a special class of cells [2]. Stem cell research has progressed rapidly since 1981, when the British scientist Martin Evans and the American scientist Gail Martin succeeded in isolating and culturing mice embryonic stem cells (ESCs). In the late 1990s, American scientist James Thomson first isolated human ESCs, revealing the potential for a pluripotent stem cell that would be a source of various germ layers and new organs Arguably, this recognition of the pluripotency of human stem cells triggered the exponential developments in stem cell research seen over the last two decades. Regenerative medicine has emerged as a new discipline, and its
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".