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Special Considerations in Wound Bed Preparation 2011

2011· review· en· W2107877403 on OpenAlex
R. Gary Sibbald, Laurie Goodman, Kevin Woo, D Krasner, Hiske Smart, Gulnaz Tariq, Elizabeth A. Ayello, Robert Burrell, David Keast, Dieter Mayer, Linda Norton, Richard Salcido

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in Skin & Wound Care · 2011
Typereview
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsPublic Health Ontario
Fundersnot available
KeywordsMedicineWound careContinuing educationCompetence (human resources)Intensive care medicineDebridement (dental)Wound healingNursingSurgeryMedical education

Abstract

fetched live from OpenAlex

In Brief PURPOSE: To enhance the learner's knowledge and competence in wound bed preparation. 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: Assess wounds to classify them and determine prognosis. Apply evolving evidence regarding effective wound bed preparation and recommend patient-specific therapy. This article builds and expands upon the concept of wound bed preparation introduced by Sibbald et al 2000 as a holistic approach to wound diagnosis and treatment of the cause and patient-centered concerns such as pain management, optimizing the components of local wound care: Debridement, Infection and persistent Inflammation, along with Moisture balance before Edge effect for healable but stalled chronic wounds. This continuing education activity builds and expands upon the concept of wound bed preparation introduced by Sibbald et al as a holistic approach to wound diagnosis and treatment of the cause and the various patient-centered concerns.

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.

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 score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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)0.0010.000

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 head0.051
GPT teacher head0.398
Teacher spread0.347 · 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