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Record 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 on OpenAlexaboutno aff
Nader Rifai

Bibliographic record

VenueClinical Chemistry · 2018
Typeeditorial
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsnot available
Fundersnot available
KeywordsMedical laboratoryCertificationMedical educationListing (finance)Journal clubChemistVariety (cybernetics)Library scienceMedicineEngineering ethicsEngineeringChemistryPolitical scienceComputer scienceArtificial intelligencePathologyBusiness

Abstract

fetched live from 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.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.038
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0020.001
Research integrity0.0060.013
Insufficient payload (model declined to judge)0.0380.030

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 head0.085
GPT teacher head0.485
Teacher spread0.401 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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
Published2018
Admission routes1
Has abstractyes

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