Reconstruction of Diaphragmatic Defects With Human Acellular Dermal Matrix
Bibliographic record
Abstract
Large diaphragmatic defects present a reconstructive challenge, often necessitating the use of synthetic materials. We report our experience reconstructing large diaphragmatic defects using human acellular dermal matrix (HADM). Patients unable to undergo primary repair of diaphragmatic defects from 2009 to 2013 were reconstructed using HADM. A chart review was performed to investigate immediate and late post-operative outcomes. Construct stability was assessed with repeat imaging. In addition, a literature review was performed to identify studies in which HADM had been used for diaphragm repair. Four patients required reconstruction of large hemi-diaphragmatic defects. All patients had chest tubes placed, which remained in situ from 4 to 10 days post-operatively. Two patients also had drains in dead space surrounding HADM; these were removed between 6 and 9 days post-procedure. Length of hospital stay ranged from 8 to 65 days. Post-operative complications were seen in 2 patients: surgical site cellulitis and failure of extubation due to persistent respiratory failure. There were no adverse events related to HADM, and all patients remained disease free without evidence of repair failure on radiographic follow-up, ranging from 14 to 62 months. The literature review identified 3 studies in which all diaphragms repaired with HADM remained intact without need for explantation despite common post-operative complications including fluid collections and surgical site infections. Diaphragm reconstruction with HADM is limited to a small number of patients and modest follow-up periods; the neodiaphragms appear durable in contaminated fields, without evidence of repair failure. Our results, and previously published data, indicate HADM is a reasonable option for diaphragm repair.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".