Utilizing International Consensus Panel Recommendations and a Clinical Decision Tree to Guide Selection of Negative Pressure Wound Therapy
Bibliographic record
Abstract
INTRODUCTION: A variety of NPWT products have become commercially available in the last 30 years. Utilizing advanced wound therapies appropriately can improve patient outcomes and decrease health care expenditures. Due to the increasing number of available product options, Hurd and colleagues published 10 Consensus Statements and a clinical decision tree to provide guidance on how and when to use NPWT and when to transition between device types. OBJECTIVE: To demonstrate the applicability of the consensus panel's statements and the clinical decision tree, 2 clinicians in the United States and Canada explored the benefits of applying these recommendations into their routine wound management practice. MATERIALS AND METHODS: Case studies were collected and reviewed in accordance with the Consensus Statements and clinical decision tree. RESULTS: Case presentations illustrate the application of the consensus panel's guidance through the prescribing of the NPWT products utilized as standard of care within both facilities. CONCLUSION: Utilizing NPWT devices according to the consensus panel recommendations and the clinical decision tree may assist in optimizing care delivery to patients and address logistical and economic efficiencies.
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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 | 0.165 | 0.285 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".