The bridge between transplantation and regenerative medicine: Beginning a new Banff classification of tissue engineering pathology
Bibliographic record
Abstract
The science of regenerative medicine is arguably older than transplantation-the first major textbook was published in 1901-and a major regenerative medicine meeting took place in 1988, three years before the first Banff transplant pathology meeting. However, the subject of regenerative medicine/tissue engineering pathology has never received focused attention. Defining and classifying tissue engineering pathology is long overdue. In the next decades, the field of transplantation will enlarge at least tenfold, through a hybrid of tissue engineering combined with existing approaches to lessening the organ shortage. Gradually, transplantation pathologists will become tissue-(re-) engineering pathologists with enhanced skill sets to address concerns involving the use of bioengineered organs. We outline ways of categorizing abnormalities in tissue-engineered organs through traditional light microscopy or other modalities including biomarkers. We propose creating a new Banff classification of tissue engineering pathology to standardize and assess de novo bioengineered solid organs transplantable success in vivo. We recommend constructing a framework for a classification of tissue engineering pathology now with interdisciplinary consensus discussions to further develop and finalize the classification at future Banff Transplant Pathology meetings, in collaboration with the human cell atlas project. A possible nosology of pathologic abnormalities in tissue-engineered organs is suggested.
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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.005 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.013 | 0.034 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.009 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 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".