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Enregistrement W2897395127 · doi:10.1373/clinchem.2018.296798

AACC Learning Lab for Laboratory Medicine on NEJM Knowledge+: Clinical Chemistry Recognizes the Contributors

2018· editorial· en· W2897395127 sur OpenAlexaboutno aff
Nader Rifai

Notice bibliographique

RevueClinical Chemistry · 2018
Typeeditorial
Langueen
DomaineMedicine
ThématiqueClinical Laboratory Practices and Quality Control
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedical laboratoryCertificationMedical educationListing (finance)Journal clubChemistVariety (cybernetics)Library scienceMedicineEngineering ethicsEngineeringChemistryPolitical scienceComputer scienceArtificial intelligencePathologyBusiness

Résumé

récupéré en direct d'OpenAlex

The cover of this issue of Clinical Chemistry features authors of the AACC Learning Lab for Laboratory Medicine on NEJM Knowledge+, known as the Learning Lab, to recognize their contribution to the program and to our profession. More than 90 clinical laboratory scientists and physicians from the US, UK, Canada, Australia, Iceland, Denmark, Norway, Croatia, and Singapore have participated in building this program. See Fig. 1 for the hierarchy of the program and authors' names. Over the past decade, Clinical Chemistry has developed a variety of educational features and programs, including the Clinical Chemistry Trainee Council, Clinical Case Studies, Journal Club, Q&A articles, Guide to Scientific Writing, and multiple clinical teasers series. However, the Learning Lab is the Journal's most ambitious endeavor. This program is useful for laboratory medicine professionals in hospital laboratories, commercial laboratories, and the in vitro diagnostics industry to help them remain abreast of current knowledge in the field, maintain certification by obtaining the required credits, assess competency, and prepare for a certification examination. The Learning Lab is a collaborative effort between NEJM Group, the publisher of the New England Journal of Medicine; AACC, the publisher of Clinical Chemistry; and Area9 Lyceum, a global leader in education technology. This cloud-based educational program uses the concept of adaptive learning, the closest method to personalized education. Sophisticated computer algorithms allow the platform to interact with the learner and quickly identify the areas in which the learner is deficient. Then it provides targeted learning materials to correct the deficiency. Such a personalized approach enables efficient learning in small blocks of time. Because the program can be accessed via mobile devices, the learner can benefit from this added flexibility. At present, approximately 40 courses have been released and are available to learners. When completed, this curriculum-based program will consist of over 120 courses in the 6 disciplines of laboratory medicine: clinical chemistry, laboratory genomics, hematology/coagulation, transfusion medicine, microbiology, and clinical immunology. Building a course is not a trivial matter; it takes a course author over 300 h to complete the task. Each course consists of 100 to 150 granular learning objectives; every learning objective is coupled with 2 probes and a learning resource. The probes are the actual questions and can be presented in 1 of 9 different formats, including multiple choice, fill-in-the-blank, matching and categorizing, and clinical cases. Morphology images, tables, chromatograms, and figures can be used in the probes and the learning resources to enhance the learning experience, particularly in image-rich courses such as mycology, parasitology, and hematopathology. The Learning Lab editors are involved in the entire process and work closely with each course author throughout the course development period. Like all NEJM Knowledge+ products, the Learning Lab courses go through a rigorous internal and external review process followed by a beta-testing evaluation conducted by 3 to 5 individuals. The primary audience for this program is laboratory medicine professionals at all levels (MD, PhD, and clinical laboratory scientists). The program content can readily be split into multiple levels on the basis of difficulty (e.g., basic, intermediate, and advanced) to allow tailoring of the initial knowledge level assumed by the software to a particular group of learners. The secondary audience includes clinicians and other health professionals. Because courses are built in a granular fashion and the program is content-rich, specific courses targeting a medical specialty or group can easily be constructed. All course authors and Learning Lab editors have participated in creating this program as volunteers. Considering the ever-increasing professional expectations and demands on clinical laboratory scientists and physicians and the considerable efforts required in building a course, the commitment shown by the authors and editors is nothing short of admirable; their dedication is a true testament to their professionalism and generosity. In addition, more than 120 faculty members, residents, and fellows have participated in reviewing and performing the beta-testing evaluation of these courses. Clinical Chemistry and its partners—NEJM Group, AACC, and Area9 Lyceum—are eternally grateful to the Learning Lab editors, course authors, reviewers, and beta testers for their contribution and efforts and promise to work tirelessly to promote this program and integrate it into laboratory medicine curriculum and practice internationally.

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)0,017
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: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,038
Score d'incertitude au seuil0,127

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,017
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0020,001
Communication savante0,0050,003
Science ouverte0,0020,001
Intégrité de la recherche0,0060,013
Charge utile insuffisante (le modèle a refusé de juger)0,0380,030

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 Opus0,085
Tête enseignante GPT0,485
Écart entre enseignants0,401 · 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
GenreÉditorial

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
Publié2018
Routes d'admission1
Résumé présentoui

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