Impact of the Cardiac Troponin Testing Algorithm on Excessive and Inappropriate Troponin Test Requests
Bibliographic record
Abstract
Cardiac troponin (cTn) is a key biomarker for the assessment of myocardial injury, but overutilization of this test has increased workload and costs. We developed and implemented an algorithm to eliminate excessive utilization. Significant reduction was observed after the implementation of the algorithm in total cTnI requests (29.9%; P = .007), requests from outpatient clinics (70.7%; P = .003), and other wards (42.8%; P = .003). Stat requests, the number of third requests, and more than 3 requests per patient were reduced significantly by 42.8% (P = .004), 35.8% (P = .003), and 49.4% (P = .008), respectively. The test and labor costs each were reduced by 29.9% (P = .007 for each). There was no significant change in cTnI orders from emergency and critical care departments. The cTnI testing algorithm reduced unnecessary orders for cTnI tests with no reduction in meeting patients'critical needs. Reduction in unnecessary and inappropriate requests reduces labor and test costs.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".