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Dogs and November: What Do They Have in Common?

2019· article· en· W2980467041 on OpenAlexaboutno aff
Elizabeth A. Ayello, R. Gary Sibbald

Bibliographic record

VenueAdvances in Skin & Wound Care · 2019
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiabetes mellitusInsulinPhysiologyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

November has dual importance in the world of skin and wound care: it is a time to raise awareness about diabetes mellitus (DM; November is National Diabetes Month and November 14 is World Diabetes Day) and pressure injuries (PIs; Thursday, November 21 is World Wide PI Prevention Day). Dogs were used for key research discoveries in both. Early work on the discovery of insulin started with a German scientist, Dr Paul Langerhans.1 He identified two types of pancreatic cells, including the Langerhans islet cells. Six years later, two other German investigators, Oskar Minkowski (a physiologist and pathologist) and Joseph von Mering (a physician) removed a dog’s pancreas, resulting in an elevated blood glucose and metabolic changes similar to the physiologic changes in a person with diabetes. In 1916, a Romanian physician from Bucharest, Nicolae Constantin Paulescu, isolated and injected aqueous pancreatic extract into a diabetic dog with a normalizing effect on blood sugar levels. Most scientists and clinicians are familiar with the work of Sir Frederick G. Banting (a Canadian orthopedic surgeon), Charles H. Best (his chemistry skills assistant), James B. Collip (who worked on the pancreas extract to purify it), and John James Richard Macleod (expert in carbohydrate metabolism), who completed further dog experiments between 1921 and 1923 that completed the knowledge translation cycle. They injected insulin into a young boy dying with type 1 DM at the Toronto General Hospital and saved his life! The rest is history; in 1923, Banting and Macleod received the Nobel Prize for the discovery of insulin.1 November 14, Dr Banting’s birthday, is World Diabetes Day. Can you do your part to identify persons with a high-risk foot and prevent foot ulcers and lower limb amputation? In this issue, a validated General Foot Screen is presented to help clinicians in their battle against DM. This screening tool can help detect persons with a high-risk foot who may not know they have diabetes. This is especially important because only 11.6% of adults with prediabetes know they are at risk; approximately 25% of persons with prediabetes will develop type 2 DM in 3 to 5 years, with up to 70% of persons with prediabetes developing DM during their lifetime.2 In contrast with DM, PIs have been observed clinically over several centuries. The classic work by Dr Michael Kosiak3 on dog thighs (1959) is often credited as breakthrough research into the etiology of pressure as the cause of PI. Each year, on the third Thursday of November, the global community pauses to raise awareness of PI. The National Pressure Ulcer Advisory Panel has many resources, including media materials, posters, buttons, drafts of proclamations, and clinical imagery that you can use to formulate a plan for prevention, educate the public, and celebrate this event at your insitution.4 Another key November PI event will be the launch of the third edition of the PI Clinical Guideline.5 The guidelines are the result of extensive collaboration of many healthcare professionals from numerous associations and stakeholders around the world who have synthesized current evidence into one document. If you cannot be in California for this landmark conference, be sure to go to the National Pressure Ulcer Advisory Panel website and download the guideline information once it is released. Fortunately, the use of dogs in scientific research has decreased since the start of the century, commensurate with the increase in humane laws implemented around the world to protect man’s best friend.6 That said, we wish to congratulate the human researchers, clinicians, educators, and funders who continue to work together to provide the evidence and knowledge that results in improved patient outcomes.Elizabeth A. Ayello, PhD, MS, BSN, RN, CWON, ETN, MAPWCA, FAANR. Gary Sibbald, MD, DSc (Hons), MEd, BSc, FRCPC (Med Derm), FAAD, MAPWCA, JM

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.540

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 communication0.0000.001
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 head0.006
GPT teacher head0.291
Teacher spread0.286 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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