Central role of laboratory medicine in public health and patient care
Bibliographic record
Abstract
Clinical laboratories play a vital role in the healthcare system. Objective medical data provided by clinical laboratories supports approximately 60-70% of clinical decisions, however, evidence supporting this claim is poorly documented and laboratories still lack visibility, despite their indisputable impact on patient care and public health. The International Federation for Clinical Chemistry and Laboratory Medicine (IFCC) Task Force on Outcome Studies in Laboratory Medicine (TF-OSLM) was recently developed to support directed research evaluating the role of laboratory medicine on clinical outcomes. Establishing and documenting this evidence is key to enhance visibility of the field in the eye of the public and other healthcare professionals together with optimizing patient outcomes and health care system operations. In this review, we discuss four areas that exemplify the contribution of laboratory medicine directly to patient care. This includes high-sensitivity cardiac troponin (hs-cTn) and N-terminal pro-B-type natriuretic peptide/B-type natriuretic peptides (NT-proBNP/BNP) for the diagnosis and prognosis of myocardial infarction and heart failure, respectively, and procalcitonin for the management of sepsis and antibiotic stewardship. Emerging markers of traumatic brain injury and the role of laboratory medicine in the fight against the COVID-19 pandemic are discussed along with an introduction to plans of IFCC TF-OSLM.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".