Lung allograft standardized histological analysis (LASHA) template: A research consensus proposal
Bibliographic record
Abstract
BACKGROUND: Routine monitoring of lung-transplanted patients is crucial for the identification of immunological and non-immunological complications. Determining the etiology of acute allograft dysfunction, particularly in alloimmune-mediated disorders, relies heavily on the lung biopsy with histopathologic analysis. Standardization of the pathologic diagnosis of rejection (e.g., cellular and antibody-mediated) is based on consensus statements and guidelines, indicating the importance of a multidisciplinary approach to achieve a definitive etiological diagnosis. In addition to these statements and guidelines, refinements and standardizations are feasible through systematic analysis morphological, immunophenotypic and molecular alterations observed in transbronchial biopsies. This study is to identify key morphologic features to be assessed, select consistent and reproducible terminology for each histological feature, and provide standardized definitions for pathological assessment and grading. METHODS: A template was created by experts in lung transplantation including pathologists, pulmonologists, immunologists. An initial draft was circulated, followed by discussions and multiple revisions by email and conference calls. RESULTS: The "lung allograft standardized histological analysis - LASHA" template was created and structured as multiple-choice questions with number of fields to be filled in to allow for standardization of results and easy transfer into a future electronic spreadsheet. CONCLUSION: This template will help facilitate multicenter studies through a uniform protocol and correlations with new diagnostic modalities. After validation in large-scale studies, an optimized template could be included in routine clinical practice to enhance graft assessment and medical decision-making.
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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 | 0.256 | 0.267 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".