Negative pressure wound therapy with instillation: International consensus guidelines update
Bibliographic record
Abstract
The use of negative pressure wound therapy with instillation and dwell time (NPWTi-d) has gained wider adoption and interest due in part to the increasing complexity of wounds and patient conditions. Best practices for the use of NPWTi-d have shifted in recent years based on a growing body of evidence and expanded worldwide experience with the technology. To better guide the use of NPWTi-d with all dressing and setting configurations, as well as solutions, there is a need to publish updated international consensus guidelines, which were last produced over 6 years ago. An international, multidisciplinary expert panel of clinicians was convened on 22 to 23 February 2019, to assist in developing current recommendations for best practices of the use of NPWTi-d. Principal aims of the meeting were to update recommendations based on panel members' experience and published results regarding topics such as appropriate application settings, topical wound solution selection, and wound and patient characteristics for the use of NPWTi-d with various dressing types. The final consensus recommendations were derived based on greater than 80% agreement among the panellists. The guidelines in this publication represent further refinement of the recommended parameters originally established for the use of NPWTi-d. The authors thank Karen Beach and Ricardo Martinez for their assistance with manuscript preparation.
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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 | 0.032 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".