Bedside Clinical Observations Stimulate COVID-19-Related Re-examination
Bibliographic record
Abstract
We are already more than halfway through the year, and we continue to explore themes of diversity and the shift back to in-person practice while retaining the benefits of virtual and hybrid healthcare-delivery models. Diversity can have many different meanings. In this month’s issue, diversity includes having articles by authors from five countries: Brazil, Canada, China, Italy, and the United States. These authors offer insights on a variety of patient populations (eg, neonatal/pediatric, adult) and wound types (eg, hospital-acquired pressure injuries, incontinence-associated dermatitis, peristomal wounds), and report on unusual cases and new care strategies. In keeping with themes of diversity and inclusion, there has been increasing interest in assessing people across the full spectrum of Fitzpatrick skin tone classifications. Girasol and colleagues provide new skin tone data from 18 participants. They evaluated the intra-examiner and inter-examiner reliability of two raters using a low-cost commercial device to measure skin tone, moisture, and oiliness. They also explored associations with the Fitzpatrick scale and reported moderate to high reliability (0.747–0.971). Between the examiners there were moderate to large associations for skin tone but lower co-rater association for moisture level documentation. Heerschap and Wiesenfeld report on the learning preferences of acute care nurses (N = 47). Nurses tended to prefer one-on-one bedside education and described the importance of varying educational techniques by topic, ensuring appropriate time of day for education, and preferring shorter educational sessions over time. The most reported learning styles were active, sensing, visual, and a balanced approach to sequential and global learning. The one-on-one clinical teaching model is well suited for mentorship and could be possible with blended models of clinical education but would require increased staffing for patient care responsibilities. Delmore and colleagues designed three questionnaires to examine COVID-19 challenges for pressure injury (PI) care. Two of these surveys were directed at manufacturers, distributors, and other supply chain personnel. The first survey examined support surface acquisition and products for institutions and the second survey addressed challenges in meeting supply chain needs with limited access to healthcare institutions. The third survey was designed to be answered by individuals in an acute care facility who had a role in procuring, obtaining, or using support surfaces and skin and wound care products. This survey focused on healthcare worker’s experiences with support surface and skin and wound care product availability and solutions to prevent and treat PIs in US hospitals. From the 174 respondents three themes emerged: differences in expectations of supply chain staff and nurses, inappropriate product substitution without consulting clinical staff, and preparedness. This month’s continuing education article discusses the important topic of periwound wound dermatitis with particular emphasis on allergic contact dermatitis to common allergens including fragrances and preservatives. The author reviews the role of patch testing to diagnose and identify causative allergens and provide a helpful algorithm to simplify the process of identifying and treating lower leg dermatitis. If patch tests are not feasible (eg, during the COVID-19 pandemic), providers can use the repeat open application test on normal skin of the flexural aspect of the forearm. Use a skin marker to make a circle the size of a silver dollar. Apply the suspected topical preparation twice a day for 48 to 72 hours. If a red tuberculin-test-like reaction occurs, that is a positive result for contact allergy to one of the components and the preparation should be avoided; perform subsequent patch tests identifying the causative agent(s). We hope our readers will keep sharing their clinical strategies so that patients worldwide will benefit.Elizabeth A. Ayello, PhD, MS, RN, CWON, MAPWCA, FAANR. Gary Sibbald, MD, MEd, FRCPC, FAAD, MAPWCA, JM
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".