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Record W4210700779 · doi:10.12968/jowc.2022.31.sup1.s1

Implementation of Wound Hygiene in clinical practice: early use of an antibiofilm strategy promotes positive patient outcomes

2022· article· en· W4210700779 on OpenAlexaff
Chris Murphy, Beata Mrozikiewicz‐Rakowska, Izabela Kuberka, Leszek Czupryniak, Paz Beaskoetxea Gómez, Melina Vega de Céniga, Angela Walker, Annabelle Tomkins, Jenny Hurlow, Raymond Abdo, Sara Sandroni, Elisa Marinelli

Bibliographic record

VenueJournal of Wound Care · 2022
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineHygieneWound careIntensive care medicineSpecialtyHealth careWound healingQuality of life (healthcare)Clinical PracticeNursingSurgeryFamily medicinePathology

Abstract

fetched live from OpenAlex

Non-healing wounds are devastating for patients, potentially causing long-term morbidity and an impaired quality of life. They also incur a huge health economic burden for health-care services. Understanding of the causes of non-healing wounds has increased significantly. While the need to address the underlying aetiology has always been acknowledged, the role of biofilm in delaying or preventing healing is now accepted. There is a consensus on the need to debride the wound to remove biofilm and then prevent its reformation, to kickstart healing. The potential benefits of incorporating an antibiofilm component within the wound bed preparation framework are clear. However, such a strategy needs to be flexible enough so that it can be implemented by all practitioners, regardless of their expertise or specialty. Wound Hygiene does this. This supplement describes the Wound Hygiene protocol, and includes a selection of case studies on different wound types, demonstrating its ease of use and effectiveness in clinical 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.050
GPT teacher head0.423
Teacher spread0.373 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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