Banff Lung Report: Current knowledge and future research perspectives for diagnosis and treatment of pulmonary antibody-mediated rejection (AMR)
Bibliographic record
Abstract
The Lung session of the 2017 14th Banff Foundation for Allograft Pathology Conference, Barcelona focused on the multiple aspects of antibody-mediated rejection (AMR) in lung transplantation. Multidimensional approaches for AMR diagnosis, including classification, histological and immunohistochemical analysis, and donor- specific antibody (DSA) characterization with their current strengths and limitations were reviewed in view of recent research. The group also discussed the role of tissue gene expression analysis in the context of unmet needs in lung transplantation. The current best practice for monitoring of AMR and the therapeutic approach are summarized and highlighted in this report. The working group reached consensus of the major gaps in current knowledge and focused on the unanswered questions regarding pulmonary AMR. An important outcome of the meeting was agreement on the need for future collaborative research projects to address these gaps in the field of lung transplantation. The Lung session of the 2017 14th Banff Foundation for Allograft Pathology Conference, Barcelona focused on the multiple aspects of antibody-mediated rejection (AMR) in lung transplantation. Multidimensional approaches for AMR diagnosis, including classification, histological and immunohistochemical analysis, and donor- specific antibody (DSA) characterization with their current strengths and limitations were reviewed in view of recent research. The group also discussed the role of tissue gene expression analysis in the context of unmet needs in lung transplantation. The current best practice for monitoring of AMR and the therapeutic approach are summarized and highlighted in this report. The working group reached consensus of the major gaps in current knowledge and focused on the unanswered questions regarding pulmonary AMR. An important outcome of the meeting was agreement on the need for future collaborative research projects to address these gaps in the field of lung transplantation.
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 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".