Banff ’09 Meeting Report: Antibody Mediated Graft Deterioration and Implementation of Banff Working Groups
Bibliographic record
Abstract
The 10th Banff Conference on Allograft Pathology was held in Banff, Canada from August 9 to 14, 2009. A total of 263 transplant clinicians, pathologists, surgeons, immunologists and researchers discussed several aspects of solid organ transplants with a special focus on antibody mediated graft injury. The willingness of the Banff process to adapt continuously in response to new research and improve potential weaknesses, led to the implementation of six working groups on the following areas: isolated v-lesion, fibrosis scoring, glomerular lesions, molecular pathology, polyomavirus nephropathy and quality assurance. Banff working groups will conduct multicenter trials to evaluate the clinical relevance, practical feasibility and reproducibility of potential changes to the Banff classification. There were also sessions on quality improvement in biopsy reading and utilization of virtual microscopy for maintaining competence in transplant biopsy interpretation. In addition, compelling molecular research data led to the discussion of incorporation of omics-technologies and discovery of new tissue markers with the goal of combining histopathology and molecular parameters within the Banff working classification in the near 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 | 0.032 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".