Histopathologic and radiologic assessment of nontransplanted donor lungs
Bibliographic record
Abstract
Donor organ shortage results in significant waiting list mortality. Donor lung assessment is currently based on donors' history, gas exchange, chest X-ray, bronchoscopy findings, and ultimately in situ inspection but remains subjective. We correlated histopathology and radiology in nontransplanted donor lungs with the clinical indications to decline the offered organ. Sixty-two donor lungs, not used for transplantation (2010-2019), were procured, air-inflated, frozen, scanned with computed tomography, systematically sampled, and histologically and radiologically assessed. Thirty-nine (63%) lungs were declined for allograft-related reasons. In 13/39 (33%) lungs, histology could not confirm the reason for decline, in an additional 8/39 (21%) lungs, histologic abnormalities were only considered mild. In 16/39 (41%) lungs, radiology could not confirm the reason for decline. Twenty-three (37%) donor lungs were not transplanted due to extrapulmonary causes, of which three (13%) lungs displayed severe histologic abnormalities (pneumonia, n = 2; emphysema, n = 1), in addition to mild emphysema in 9 (39%) lungs and minor bronchopneumonia in 1 (4%). Radiology revealed ground-glass opacities in 8/23 (35%) and emphysema in 4/23 (17%) lungs. Histopathologic and radiologic assessment of nontransplanted donor lungs revealed substantial discrepancy with the clinical reason for decline. Optimization of donor lung assessment is necessary to improve current organ acceptance rates.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".