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Optimizing the Moisture Management Tightrope with Wound Bed Preparation 2015©

2015· review· en· W2413657696 on OpenAlexaff
R. Gary Sibbald, James A. Elliott, Elizabeth A. Ayello, Ranjani Somayaji

Bibliographic record

VenueAdvances in Skin & Wound Care · 2015
Typereview
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of CalgaryWorld Wildlife Fund Canada
Fundersnot available
KeywordsMedicineWound careAntisepticChronic woundMoistureIntensive care medicineWound healingSurgeryPathology

Abstract

fetched live from OpenAlex

PURPOSE: To provide an overview of moisture management and its importance in wound care. TARGET AUDIENCE: This continuing education activity is intended for physicians and nurses with an interest in skin and wound care. OBJECTIVES: After participating in this educational activity, the participant should be better able to:1. Summarize causes and treatments for moisture balance issues of chronic wounds.2. Recognize the properties of dressings used for treatment for moisture management of chronic wounds and antiseptic agent cytotoxicity.3. Explain study findings of the effectiveness of dressing choices for treatment of chronic wounds. OBJECTIVE: To provide an overview of moisture management and its importance in wound care. The authors evaluate the impact of moisture management for optimal wound care and assess current wound management strategies relating to antisepsis and moist wound healing utilizing the wound bed preparation paradigm 2015 update. The discussion distinguishes the form and function of wound care dressing classes available for optimal moisture management. CONCLUSION: Moisture management for chronic wounds is best achieved with modern moist interactive dressings if the wound has the ability to heal.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.382
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations44
Published2015
Admission routes1
Has abstractyes

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