<i>Clinical Chemistry and Laboratory Medicine</i>: Happy 60th Anniversary
Bibliographic record
Abstract
Information disseminated by CCLM to a global audience has surely transformed the field of clinical chemistry and laboratory medicine.Specifically, CCLM has taken the lead in publishing guidelines to support clinical laboratories around the world in applying up-to-date evidence in a way that improves clinical practice.This became especially important in the context of the COVID-19 pandemic, particularly in early months when little was known about the SARS-CoV-2 virus or COVID-19 disease.IFCC recognized this critical gap, and in turn, produced several guidelines on molecular, serological, and biochemical monitoring of COVID-19, as well as biosafety measuring for preventing COVID-19 in clinical laboratories.Thanks to the outstanding support of the Editor-in-Chief, Professor Mario Plebani, and the Associate Editor, Professor Giuseppe Lippi, the IFCC Taskforce on COVID-19 formed a very productive collaboration with CCLM to publish these recommendations alongside expert opinion pieces and original research articles in a special issue of CCLM.This timely special issue became a treasured resource for laboratory medicine specialists and other healthcare workers around the world.Importantly, this issue also helped to demonstrate the vital role of clinical laboratories in both patient care and public health.Following this initial series of publications, additional manuscripts were published in CCLM by the IFCC Taskforce on other important guidelines, such as the IFCC interim guidelines on rapid point-of-care antigen testing for SARS-CoV-2 detection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.029 | 0.040 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".