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ABCDEFGHI Systematic Approach to wound assessment and management

2023· review· en· W4321611354 on OpenAlexaff
Sarah C. Hunt, Sanjay Azad

Bibliographic record

VenueNursing · 2023
Typereview
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsNOSM UniversityThunder Bay Regional Health Sciences Centre
Fundersnot available
KeywordsWound careReferralCompetence (human resources)MedicineMEDLINEIntensive care medicineNursingPsychologyPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT: The ABCDEFGHI approach introduces a systematic approach to wound care. It instructs the clinician to Ask pertinent questions, including those that may identify local and systemic Barriers to wound healing. After obtaining a thorough history, the clinician may proceed to Clean the wound and Do a physical examination, specifically looking for Exposed structures and Factors that will complicate the healing process. Good Healing strategies involving various dressings can then be implemented to promote healing. If necessary, a referral can be made to Involve specialists using various referral pathways. Information used to synthesize this approach was obtained through a review of national and international guidelines and Google Scholar, MEDLINE, and PubMed databases. The ABCDEFGHI approach to wound assessment and management is a simple and easy-to-follow guide that can be easily implemented into practice, thereby improving clinician confidence and competence in wound care.

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)0.035
metaresearch head score (Gemma)0.126
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 score0.039
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.126
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0390.023
Science and technology studies0.0020.003
Scholarly communication0.0060.005
Open science0.0040.006
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0190.005

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 head0.206
GPT teacher head0.483
Teacher spread0.278 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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