Efficiency of New Smart Instillation Technology With Negative Pressure Wound Therapy in Managing Complex Chronic and Surgical Wounds: A Case Series
Bibliographic record
Abstract
BACKGROUND: Use of negative pressure wound therapy with instillation and dwell time (NPWTi-d) of a topical wound solution has been limited in some settings due to perceptions of setup complexity. Typically, some guesswork was needed to estimate an adequate volume of solution to instill without causing leaks. A novel smart technology is recently available in certain NPWTi-d systems that automatically estimates and instills a solution volume according to wound dimensions. OBJECTIVE: To report experience with this smart instillation NPWTi-d system technology in managing 4 complex wounds containing large areas of devitalized tissue and/or yellow fibrinous slough. MATERIALS AND METHODS: NPWTi-d was applied via a reticulated open cell foam dressing with through holes (ROCF-CC). The smart instill button was selected to automatically determine a volume of topical solution to instill, followed by a 10-minute dwell time and 2-hour cycle of -125 mm Hg negative pressure. RESULTS: The average NPWTi-d duration was 17.0 days, and no air or solution leaks occurred during therapy. Dressings were changed 3 times per week. All wounds were converted to clean granulating wounds during therapy. CONCLUSION: In this case series, smart technology simplified setup and facilitated regular cleansing and removal of devitalized tissue through the ROCF-CC dressing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".