Measurement of High-Sensitivity Cardiac Troponin in Pulmonary Embolism: Useful Test or a Clinical Distraction
Bibliographic record
Abstract
The ability to predict death or other unfavorable outcomes after an acute pulmonary embolism (PE) is challenging, with current available risk score models having relatively unsatisfactory prognostic performance in this area. For example, the simplified pulmonary embolism severity index (sPESI), the most frequently used stratification tool, misclassifies a significant percentage of low- and high-risk patients. This gap in care, along with the increasing clinical availability of high-sensitivity cardiac troponin (hs-cTn) laboratory tests and the recent emphasis on detecting myocardial injury, may foster further evaluation of hs-cTn testing in patients with acute PE. Our analysis of the current scientific literature on hs-cTn in patients with acute PE identified that hs-cTn testing may provide valuable information for predicting future adverse outcomes and mortality, independently from baseline clinical risk assessment. Although the risk of an adverse event is indeed higher in patients with higher sPESI scores, cTns retain their prognostic value also in those at low risk, suggesting that a combination of hs-cTn with sPESI may provide an incremental value over assessment of either variable alone. Accordingly, the future development of updated risk stratification models, with the inclusion of laboratory tests such as hs-cTn, may represent an enhanced approach for risk stratification in patients with acute PE. Additional research, however, is needed to verify whether the combination of cTns, specifically as measured with hs-cTn assays, with other biomarkers may further improve the current capacity to efficiently manage patients with acute PE.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".