Extracorporeal Membrane Oxygenation as a Bridge to Pediatric Heart Transplantation
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
BACKGROUND: Current organ allocation algorithms direct hearts to the sickest recipients to mitigate death while waiting. This may result in lower post-transplant (Tx) survival for high-risk candidates mandating close examination to determine the appropriateness of different technologies as a bridge to Tx. METHODS AND RESULTS: We analyzed all patients (<18 years old) from the Pediatric Heart Transplant Study (PHTS) database listed for heart Tx (1993-2013) to determine the effect of extracorporeal membrane oxygenation (ECMO) support at the time of listing and the time of Tx on waitlist mortality and post-Tx outcomes. Eight percent of patients were listed on ECMO, and within 12 months, 49% had undergone Tx, 35% were deceased, and 16% were alive waiting. Survival at 12 months after listing (censored at Tx) was worse in patients on ECMO at listing (50%) compared with ventricular assist device at listing (76%) or not on ECMO or ventricular assist device at listing (76%; P<0.0001). Two hundred three (5%) patients underwent Tx from ECMO; 135 (67%) had been on ECMO since listing, and 67 (33%) had deteriorated to ECMO support while waiting. Survival after Tx was worse in patients who underwent Tx from ECMO (3 years: 64%) versus on ventricular assist device at Tx (3 years: 84%) or not on ECMO/ventricular assist device at Tx (3 years: 85%; P<0.0001). Patients transplanted from ECMO at age <1 year had the worst survival. CONCLUSIONS: Pediatric patients requiring ECMO support before heart Tx have poor outcomes. Prioritization of donor hearts to children waitlisted on ECMO warrants careful consideration because of ECMO's high pre- and post-Tx mortality.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it