The impact of first untreated subclinical minimal acute rejection on risk for chronic lung allograft dysfunction or death after lung transplantation
Bibliographic record
Abstract
Acute cellular rejection (ACR) is a significant risk factor for chronic lung allograft dysfunction (CLAD). Although clinically manifest and higher grade (≥A2) ACR is generally treated with augmented immunosuppression, management of minimal (grade A1) ACR remains controversial. In our program, patients with subclinical and spirometrically stable A1 rejection (StA1R) are routinely not treated with augmented immunosuppression. We hypothesized that an untreated first StA1R does not increase the risk of CLAD or death compared to episodes of spirometrically stable no ACR (StNAR). The cohort was drawn from all consecutive adult, first, bilateral lung transplantations performed between 1999 and 2017. Biopsies obtained in the first-year posttransplant were paired with (forced expiratory volume in 1 second FEV 1 ). The first occurrence of StA1R was compared to a time-matched StNAR. The risk of CLAD or death was assessed using univariable and multivariable Cox proportional hazards models. The analyses demonstrated no significant difference in risk of CLAD or death in patients with a first StA1R compared to StNAR. This largest study to date shows that, in clinically stable patients, an untreated first A1 ACR in the first-year posttransplant is not significantly associated with an increased risk for CLAD or death. Watchful-waiting approach may be an acceptable tactic for stable A1 episodes in lung transplant recipients.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".