Bibliographic record
Abstract
Cardiac troponins (cTns) are the most valuable and specific markers of cardiovascular diseases, including acute myocardial infarction. These biomarkers can also be used to assess the degree of myocardial damage in non-cardiac diseases that can negatively affect the cells of cardiac muscle tissue. However, in everyday clinical practice, doctors often encounter with false-positive cases of increased cTns. False-positive cases of increased cTns can contribute to incorrect diagnosis and subsequent inadequate treatment, which causes significant harm to the patient. This review discusses some common causes of a false-positive increase in the level of cTns in the blood serum. Such causes are fibrin clots, heterophilic antibodies, alkaline phosphatase, rheumatoid factor, and cross-reactions of diagnostic (anti-cTn) antibodies with skeletal troponins. Detailed attention is focused on the mechanisms of false-positive increase, and ways to identify and combat these false-positive causes of increased cTns. This has an important practical significance in modern clinical practice.
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 | 0.059 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".