Use of a Novel Epidermal Harvesting System in Resource-Poor Countries
Bibliographic record
Abstract
The 2010 earthquake in Port-au-Prince, Haiti, highlighted the need for wound care in resource-poor countries. Subsequently, the University of Miami in Florida established one of the first interprofessional wound care centers located at Bernard Mevs Hospital in the central portion of Port-au-Prince, caring for patients with acute and chronic wounds. In 2012, the authors used a novel epidermal harvesting system (CelluTome Epidermal Harvesting System; Kinetic Concepts Inc, San Antonio, Texas) to harvest epithelium to be grafted on 7 patients at the Mevs Hospital with longstanding wounds. Epidermal microblisters were obtained from each patient's thigh using the CelluTome Epidermal Harvesting System. After 35 minutes, microblisters were raised using the device harvester, and an adhesive dressing was inserted into the harvester for transfer to the wound site. In patients with lower-extremity wounds, a 2-layer compression dressing was placed over epidermal grafts. Six of the 7 wounds improved or achieved complete closure in 4 weeks. One of the patients with a 2-year-old thigh wound failed to demonstrate improvement; this may have been secondary to an inability to adequately secure the graft. All donor sites healed without any visible scarring. The authors were able to conclude that epidermal grafting may represent a viable reconstructive option for patients in resource-poor countries.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".