Notice bibliographique
Résumé
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
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,006 |
| Communication savante | 0,023 | 0,028 |
| Science ouverte | 0,004 | 0,008 |
| Intégrité de la recherche | 0,019 | 0,030 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,093 | 0,047 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».