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Enregistrement W2115888632 · doi:10.2215/cjn.04841107

Acute Kidney Injury

2008· article· en· W2115888632 sur OpenAlexaffabout
Adeera Levin, John A. Kellum, Ravindra L. Mehta

Notice bibliographique

RevueClinical Journal of the American Society of Nephrology · 2008
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Kidney Injury Research
Établissements canadiensUniversity of British ColumbiaProvidence Health Care
Organismes subventionnairesnon disponible
Mots-clésMedicineAcute kidney injuryIntensive care medicineDelphi methodDialysisAntecedent (behavioral psychology)Kidney diseaseEtiologyConceptual frameworkMEDLINEPathologyInternal medicineArtificial intelligenceComputer science

Résumé

récupéré en direct d'OpenAlex

Acute kidney injury (AKI) is widely recognized as an important predictor of morbidity and mortality and as an antecedent to chronic kidney disease (1). In the past 5 yr, there has been increasing interest in understanding this entity, at the both basic science and clinical levels. Recent advances in methodology have led to the description of both serum and urine biomarkers and the imaging of events occurring at the cellular level (2–5). Advances in understanding of and technology associated with dialysis in acute care settings have also contributed to the increasing recognition and treatment of AKI in these settings and the development of research studies to define its appropriate use (6–9). The purpose of this collection of articles is to describe the development of a research agenda, using a modified Delphi approach, that is based on a conceptual framework and a refined definition of AKI. Although each article is written in a different format, the key messages are similar: There is a limited evidence base about most aspects of AKI, and there is a need, through a concerted effort of clinicians and researchers, to address questions that will have an impact on patient outcomes. We acknowledge that AKI is a term that actually encompasses multiple etiologies. For the purposes of defining a research agenda, it is clear that an overarching term is preferred: Specific etiologies can then be more clearly investigated or evaluated within the contextual framework described here. In this edition of CJASN, the first series describe the conceptual model of AKI and how it can be used to answer specific questions, as well as remaining questions regarding the epidemiology of AKI. In particular, we stress the need for better understanding and definitions that are applicable in a multitude of situations. The next article defines the evaluation and early management of AKI, with a major emphasis on the need to distinguish between volume-responsive and volume-unresponsive AKI in a systematic way. The last two articles describe the issues related to renal replacement therapy, particularly indications for and choices of therapy, and again describe key questions related to timing of therapy, defining optimal and minimal dosages of therapy. All of the articles use the definition of AKI recently published (1). The importance of a conceptual model in which to study AKI cannot be overstated. Building on previous work in chronic kidney disease and using the expertise of basic and clinical science, a conceptual model of AKI was developed and refined, initially within one workgroup and subsequently with input from the entire 43 participants at the Vancouver AKIN meeting (September 2007). Briefly, the key aspects of the model include the description of a trajectory of disease from normal populations to at-risk populations to those with early reversible and late nonreversible disease, the ability to define a clear research agenda (both clinical and basic) at every stage of the model, and the concordance with existing accepted models of chronic kidney disease. The second article describes this model in detail. This series of articles serves to focus the community on the importance of AKI as an entity, describe the myriad of possibilities in terms of research and clinical practice opportunities, and describe the current state of knowledge. Through ongoing clinical and research initiatives and leveraging the newly established Acute Kidney Injury Network, we hope to gain an understanding of how best to prevent and treat AKI so that ultimately we are able to improve patient outcomes. The Vancouver conference and the articles in this CJASN selection build on the first AKIN conference held in Amsterdam in 2005 (1). The specific method used is described in the article by Kellum et al. (11) in this series and thus is not repeated in each article. Disclosures None.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,023
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,092
Score d'incertitude au seuil0,308

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,023
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0030,004
Études des sciences et des technologies0,0020,001
Communication savante0,0040,003
Science ouverte0,0020,005
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0920,025

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 Opus0,069
Tête enseignante GPT0,424
Écart entre enseignants0,354 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
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

Citations29
Publié2008
Routes d'admission2
Résumé présentoui

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