Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: The number of patients listed for lung transplantation largely exceeds the number of available transplantable organs because of a shortage of organ donors and a low utilization rate of lungs from those donors who are available. In recent years, novel strategies have been developed to increase the donor lung pool: improved donor management, the use of lungs from donations after cardiac death (DCD), the use of lobar lung living-donors (LLLD) and the use of ex-vivo lung perfusion (EVLP) to assess and repair injured donor lungs. RECENT FINDINGS: An adapted donor management strategy could expand the donor pool up to 20%. DCD lung transplant is an increasing part of the donor pool expansion. Outcomes after controlled DCD seem to be similar to donation after brain death. LLLD transplantation has excellent results for small and critically ill patients. EVLP treatment allows for a significant increase in the rate of suitable lungs and represents an optimal platform for lung reconditioning and specific lung therapies. SUMMARY: A significant increase in the number of available lungs for transplantation is expected in the future because of the wider use of lungs from controlled or uncontrolled DCD and LLLD lungs, and with organ-specific EVLP treatment strategies.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it