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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".