Providing direction improves function: Comparison of a radial pore-orientated acellular collagen scaffold to clinical alternatives in a surgically induced rabbit diaphragmatic tissue defect model
Bibliographic record
Abstract
Gore-Tex® is a widely used durable patch for repair of congenital diaphragmatic defects yet may result in complications. We compared Gore-Tex with a composite of a radial pore-orientated collagen scaffold (RP-Composite) and clinically used porcine small intestinal submucosa (SIS; Surgisis®) in a rabbit model for diaphragmatic hernia. The growing rabbit mimics the rapid rib cage growth and reherniation rates seen in children. We created and immediately repaired left hemidiaphragmatic defects in 6-week-old rabbits with Gore-Tex, SIS, and an RP-Composite scaffold. An additional group of rabbits had a sham operation. At 90 days, survivors more than doubled in weight. We observed few reherniations or eventrations in Gore-Tex (17%) and RP-Composite (22%) implanted animals. However, SIS failed in all rabbits. Maximum transdiaphragmatic pressure was lower in Gore-Tex (71%) than RP-Composite implanted animals (112%) or sham (134%). Gore-Tex repairs were less compliant than RP-Composite, which behaved as sham diaphragm (p < 0.01). RP-Composite induced less foreign body giant cell reaction than Gore-Tex (p < 0.05) with more collagen deposition (p < 0.001), although there was a tendency for the scaffold to calcify. Unlike Gore-Tex, the compliance of diaphragms reconstructed with RP-Composite scaffolds were comparable with native diaphragm, whereas reherniation rates and transdiaphragmatic pressure measurements were similar.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".