Management of the open abdomen using combination therapy with ABRA and ABThera systems
Bibliographic record
Abstract
BACKGROUND: The open abdomen is an increasingly used technique that is applied in a wide variety of clinical situations. The ABThera Open Abdomen Negative Pressure Therapy System is one of the most common and successful temporary closure systems, but it has limited ability to close the fascia in approximately 30% of patients. The abdominal reapproximation anchor system (ABRA) is a dynamic closure system that seems ideal to manage patients who may not achieve primary fascial closure with ABThera alone. We report on the use of the ABRA in conjunction with the ABThera in patients with an open abdomen. METHODS: We retrospectively analyzed patients with an open abdomen managed with the ABThera and ABRA between January 2007 and December 2012 at the Halifax Infirmary, QEII Health Science Centre, Halifax, Nova Scotia. RESULTS: Sixteen patients had combination therapy using the ABRA and ABThera systems for treatment of the open abdomen. After removing patients who died prior to closure, primary fascial closure was achieved in 12 of 13 patients (92%). CONCLUSION: We observed a high rate of primary fascial closure in patients with an open abdomen managed with the ABThera system in conjuction with the ABRA. Applying mechanical traction in addition to the ABThera should be considered in patients predicted to be at high risk for failure to achieve primary fascial closure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".