Factors associated with positive outcomes in 131 patients treated with gauze-based negative pressure wound therapy
Bibliographic record
Abstract
Negative Pressure Wound Therapy (NPWT) is commonly used in many surgical specialties to improve wound management and healing outcomes. This study reports the ability of gauze-based NPWT to address several treatment goals commonly defined at the onset of therapy. A prospective, multi-center, non-comparative clinical investigation was carried out using gauze-based NPWT in chronic and acute wounds. 131 patients including traumatic, post-surgical and chronic wounds were assessed. Weekly percentage reductions in wound area, depth and volume were 8.3%, 15.8% and 20.5% respectively (p < 0.001). A reduction in exudate level was observed from baseline to treatment discontinuation (p < 0.001). An increase (p = 0.007) in red granulation tissue and a decrease (p < 0.001) in non-viable tissue was observed. Baseline wound characteristics associated with slower rates of progress included chronic wound aetiologies, longer wound duration prior to NPWT and presence of diabetes as a co-morbidity. Important indicators of wounds which had improved sufficiently and no longer required NPWT included reduction in volume and exudate levels. Gauze-based NPWT can be used to address many of the treatment goals commonly defined at the onset of therapy including reduction in wound volume, management of exudate and infection status, and improvement in wound bed quality.
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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 | 0.002 |
| 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 | 0.001 |
| 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".