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.
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 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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| 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".