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Improved Communication Facilitates Chronic Wound Care for Patients, Families, and Professionals

2018· editorial· en· W2896883960 on OpenAlexaff
R. Gary Sibbald, E A Ayello

Bibliographic record

VenueAdvances in Skin & Wound Care · 2018
Typeeditorial
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of TorontoToronto Public Health
Fundersnot available
KeywordsMedicineWound careChecklistActivities of daily livingScale (ratio)Pain assessmentNursingPhysical therapyPain managementIntensive care medicinePsychology

Abstract

fetched live from OpenAlex

November is family caregiver recognition month in the US.1 This is an ideal time to reflect on skin and wound care challenges faced by our patients and their families. Implementation of the Caregiver Advise Record Enable (CARE) Act has resulted in an urgent call for professionals to facilitate care through enhanced communication and patient/caregiver education. We need to use this opportunity to develop new ways to partner with our patients and their families, especially when wounds are not healing at the expected rate.2–4 This month’s CE/CME article provides a framework or supporting structure to systematically review and optimize care for persons with nonhealing wounds. This framework could evolve into a useful checklist if the items are validated as compulsory criteria to establish a healing trajectory. The components of the framework can be divided into the Wound Bed Preparation model, including patient-centered concerns along with the treatment of the cause, ability to heal, and local wound care.5 Patient-centered concerns revolve around pain, activities of daily living, and the patients’ circle of care. If pain is categorized on an 11-point numerical rating scale (0–10, where 0 is no pain), most patients can live with a pain level of 3 or 4 and carry out activities of daily living. Wound care providers need to take coresponsibility for care negotiated with the patient and his or her circle of care. For example, patients with pain should be offered options for treating both nociceptive and neuropathic pain with adjustable medications and diversional therapies. An adequate support network can facilitate attendance at healthcare appointments, good nutrition, adherence to treatment, and a clean home environment. Most wound care professionals agree that persons with chronic wounds can present with a variety of comorbidities and factors that can impact on wound healing. The complexity of these cases requires care coordination and communication to facilitate a mutually agreeable plan of care. Wounds that are not in a healing trajectory especially require a comprehensive assessment (ideally with an interprofessional team) to establish a precise diagnosis and identify modifying factors that may or may not be correctable.6 These assessments should involve the “whole patient” and not only the “hole in the patient!” In addition to vascular function, the CE/CME discusses a number of other variables that can delay healing: Suboptimal nutrition corrected with dietary counseling and healthy eating Smoking tobacco with counseling to reduce or stop Inadequate diabetes control optimized with hemoglobin A1c as close to 7 as possible Abnormal laboratory studies (low hemoglobin, impaired liver and kidney function) Other coexisting diseases (rheumatoid arthritis, malignancy) or drugs (steroids, immunosuppressive agents) that can impair healing Clinical signs of infection that need to be treated with antimicrobial therapy Outlining key factors to consider facilitates interprofessional team communication and ultimately potentially enhances treatment. Ideally, we can heal most wounds with a framework for improved diagnosis, identification of modifiable factors, and evaluation of patient response to the treatment. The quality of life can also be improved for patients with nonhealable wounds by addressing patient-centered concerns and concentrating on the needs of the whole person.FigureR. Gary Sibbald, BSc, MD, DSc (Hons), MEd, FRCPC (Med Derm), FAAD, MAPWCA, JMFigureElizabeth A. Ayello, PhD, RN, CWON, ETN, MAPWCA, FAAN

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.441
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.328
Teacher spread0.321 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2018
Admission routes1
Has abstractyes

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