AACC Learning Lab for Laboratory Medicine on NEJM Knowledge+: Clinical Chemistry Recognizes the Contributors
Bibliographic record
Abstract
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. Fig. 1. Hierarchy of the Learning Lab program with a listing of program editors and authors. 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. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.354 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".