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The Role of Moisture Balance in Wound Healing

2007· review· en· W1998860648 on OpenAlexaff
Denis Okan, Kevin Woo, Elizabeth A. Ayello, Gary Sibbald

Bibliographic record

VenueAdvances in Skin & Wound Care · 2007
Typereview
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsWomen's College HospitalUniversity of TorontoRegistered Nurses' Association of OntarioMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineWound healingBalance (ability)SurgeryPhysical therapy

Abstract

fetched live from OpenAlex

In Brief PURPOSE To provide an overview of moisture balance and its importance in wound healing. TARGET AUDIENCE This continuing education activity is intended for physicians and nurses with an interest in wound care. OBJECTIVES After reading this article and taking the test, the reader should be able to: Discuss the wound healing process and wound assessment. Describe the types of dressings available and how they meet the needs of the individual patient. This continuing education activity focuses specifically on moisture balance in the wound, and it is the third in a 4-part series that will address the components of wound bed preparation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.020
GPT teacher head0.379
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations227
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Skin & Wound CareSame topicWound Healing and TreatmentsFrench-language works237,207