Il punto sul controllo di qualità di troponina (cTn) per esami in Point-of-Care Testing
Bibliographic record
Abstract
This brief review aims to make the point on the quality assurance of Point-of-Care Testing systems for troponin, with conventional methods (POCT cardiac troponin [cTn]) and high-sensitivity methods (POCT high-sensitive cardiac troponin [hs-cTn]), in relation to the main reference standards of International Organization for Standardization (ISO 15189:2022 and ISO/TS 22853:2019) and Clinical Laboratory and Standards Institute (CLSI EP23A, EP31, EP09 and POCT04). The activities of internal quality control (IQC), external quality assurance (EQA), and comparability of POCT (hs)-cTn methods/instruments, aimed at analytical and medical goals for patient care, are analyzed by comparing the latest guidelines of International Federation of Clinical Chemistry (IFCC), Australasian Association of Clinical Biochemistry and Laboratory Medicine (AACB), Canadian Society of Clinical Chemists (CSCC), American Association of Clinical Chemistry (AACC; now Association for Diagnostics and Laboratory Medicine, ADLM), NOKLUS (The Norwegian Organization for Quality Improvement of Laboratory Examinations), and original positions of the SIPMeL Myocardial Markers Working Group (Gruppo di Studio Marcatori Miocardici della Società Italiana di Patologia Clinica e Medicina di Laboratorio, GdS-MM SIPMeL) related to the frequency and type of IQCs and alternatives to traditional EQAs.
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 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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".