Highlights from the clinical trials in organ transplantation (CTOT)-20 and CTOT-22 Consortium studies in lung transplant
Bibliographic record
Abstract
Long-term survival after lung transplant lags behind that of other commonly transplanted organs, reflecting the current incomplete understanding of the mechanisms involved in the development of posttransplant lung injury, rejection, infection, and chronic allograft dysfunction. To address this unmet need, 2 ongoing National Institute of Allergy and Infectious Disease funded studies through the Clinical Trials in Organ Transplant Consortium (CTOT) CTOT-20 and CTOT-22 were dedicated to understanding the clinical factors and biological mechanisms that drive chronic lung allograft dysfunction and those that maintain cytomegalovirus polyfunctional protective immunity. The CTOT-20 and CTOT-22 studies enrolled 800 lung transplant recipients at 5 North American centers over 3 years. Given the number and complexity of subjects included, CTOT-20 and CTOT-22 utilized innovative data transfers and capitalized on patient-entered data collection to minimize site manual data entry. The data were coupled with an extensive biosample collection strategy that included DNA, RNA, plasma, serum, bronchoalveolar lavage fluid, and bronchoalveolar lavage cell pellet. This Special Article describes the CTOT-20 and CTOT-22 protocols, data and biosample strategy, initial results, and lessons learned through study execution.
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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 | 0.147 | 0.289 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".