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Record W2909802653 · doi:10.1111/iwj.13064

A new year: The same challenge

2019· editorial· en· W2909802653 on OpenAlexaboutno aff
Douglas Queen

Bibliographic record

VenueInternational Wound Journal · 2019
Typeeditorial
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStress (linguistics)Wound careHealth carePhraseSurgeryLawLinguistics

Abstract

fetched live from OpenAlex

As many of our readers will know, I usually write the first editorial for the year from some exotic location. This year, I decided to make it Toronto. For those who know me personally, that is my home. Irrespective of my accent! Exotic? well as I write, it is like −6°C, so not quite! Entering 2019 means that it has been 56 years since George Winter first published his research on moist wound healing.1 But it is only in the past 10 to 15 years that we have started to use the phrase “woundology” to look at the clinical specialisation of managing those with wounds.2, 3 So, have we come a long way in my lifetime? Yes, I will be 58 in a few months. Or do we have a significant way to go? Probably the latter. I remember, as a young man, joining ConvaTec, which led the way of the true emergence of the moist wound-healing concept. At that time, I believed we would change the world of wound care, and within 10 years moist wound healing would be the only practice. How wrong was I? Today, some three or more decades later, around 50%, at best,4 of those with wounds receive some form of “advanced” wound care. Much of the practice remains the same. In addition, our understanding of how wounds heal has not advanced significantly. Our educational challenges remain the same at best but are probably more significant as health care systems drive the delivery of wound care down the “skill chain,”5 all on the premise of saving cost. Those with more experience clearly understand that the opposite happens in that the costs of managing such patients increase.6 So, in 2019, our challenges continue. But as you may have read in some of the more recent editorials,7-9 the major change is likely to come as a result of technology8 and a new generation of caregivers.9 The exciting part is that change will be driven by the desire of health care to become more technology focused and will most likely occur more rapidly than the past three decades. Why would this be the case? Most health care systems have embraced electronic health records, and this has become a major game changer in the clinical workflow. With a technological component focused on wound care, both clinically and in the workflow, and with integration into any EHR/EMR systems, this will significantly change the delivery of wound care across the health care continuum.10 We have all heard of “Big Data,” but who truly understands it? Or more importantly, its potential impact on our working environment and patients.11 Such an approach has already revolutionised other clinical areas (eg, detection of diabetic retinopathy).12 One of the biggest challenges for such an approach will be the contextualisation of the data as wound care is not a standardised practice for the most part. However, that is the exciting impact that Big Data can have on the understanding of the “real world” of wound care and provide the necessary background to evolve a more standardised, clinical specialisation – that of “woundology.”2, 13

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.076
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.315
Teacher spread0.300 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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