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The ABCs of Skin Care for Wound Care Clinicians

2009· article· en· W2030869750 on OpenAlexaff
Kevin Woo, R. Gary Sibbald

Bibliographic record

VenueAdvances in Skin & Wound Care · 2009
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsWomen's College HospitalRegistered Nurses' Association of Ontario
Fundersnot available
KeywordsMedicineContinuing educationWound careSkin careContinuing medical educationContinuing careReading (process)MEDLINEDermatologyIntensive care medicineNursingMedical education

Abstract

fetched live from OpenAlex

In Brief PURPOSE To provide the wound care practitioner with an overview of the nature and treatment of dermatitis. TARGET AUDIENCE This continuing education activity is intended for physicians and nurses with an interest in skin and wound care. OBJECTIVES After reading this article and taking this test, the reader should be able to: Describe the pathophysiology of dermatitis. Discuss the characteristics that help to differentiate common types of dermatitis. Identify appropriate treatment strategies for dermatitis. In this continuing education activity, the authors discuss the different types of dermatitis and eczema treatments, and provide concrete strategies and basic information for busy wound care clinicians to integrate into practice.

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)0.006
metaresearch head score (Gemma)0.040
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0070.006
Open science0.0010.006
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0690.031

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 head0.008
GPT teacher head0.332
Teacher spread0.324 · 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
GenreEmpirical

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

Citations16
Published2009
Admission routes1
Has abstractyes

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