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Enregistrement W2980467041 · doi:10.1097/01.asw.0000604080.25571.f5

Dogs and November: What Do They Have in Common?

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

Notice bibliographique

RevueAdvances in Skin & Wound Care · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueDiabetic Foot Ulcer Assessment and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineDiabetes mellitusInsulinPhysiologyInternal medicineEndocrinology

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,539
Score d'incertitude au seuil0,540

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,006
Tête enseignante GPT0,291
Écart entre enseignants0,286 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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