The LOEX perspective on the role of tissue engineering in regenerative medicine
Bibliographic record
Abstract
There is little doubt that tissue engineering is an extraordinary addition to the therapeutic armamentarium of reparative tissue techniques. The dilemma of adequately repairing either failing or traumatized organs has been looming larger as patients either become older or are in dire need of grafts. This is a most significant approach to repair, replace or regenerate organs in a fashion that could not have been envisioned in any way 40 years ago. Tissue engineering creates a radically new chapter in regenerative biomedicine for it is now deemed possible to reconstruct in the laboratory human living tissues and organs for either in vivo, ex vivo and even in vitro applications [1–7]. This new area of biotechnology is very multidisciplinary and thus demands combined effort from biologists, clinicians, scientists and engineers. When the aim of tissue engineers is to obtain substitutes for in vivo application, the biological and mechanical functions are of utmost importance. According to some subdivisions of the field, one can essentially demand a biological function, as in cell therapy, or principally a mechanical function, as is seen in the use of tissue biomaterials [8]. The tissue-engineered substitutes are three-dimensional constructs that can be implanted into the human body leading to a rapid host integration and immune acceptance. Thus, these substitutes must have a minimum of biological and mechanical functions for such a smooth integration of reparative role.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 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".