Evaluation of conventional troponin I testing for the detection of myocardial dysfunction in children
Bibliographic record
Abstract
Objectives: Troponin is a marker of myocardial injury but is not well studied in children. Our primary objective was to ascertain the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of conventional troponin I for the detection of acute myocardial dysfunction in previously healthy children. Our secondary objective was to identify clinical predictors of myocardial dysfunction in the setting of elevated troponin. Study Design: This was a retrospective chart review in a single, paediatric, tertiary care centre of troponin tests performed in all admitted children over a 4-year period. Demographics, symptoms, signs, chest x-ray, ECG, and echocardiogram abnormalities were documented. Myocardial dysfunction was presumed to be absent when the patient had a normal cardiac assessment, with or without echocardiography, and did not re-present. Results: From January 2014 through December 2017, 566 patients had troponin tested as a screen for myocardial injury. Troponin was positive in 38 of 566 cases (6.7%). Myocardial dysfunction was detected in 9 of 566 cases (1.6%). Troponin was elevated in six of nine cases of myocardial dysfunction. The sensitivity of conventional troponin I for detecting acute myocardial dysfunction was 66% (95% confidence interval [CI] 30 to 93%). The specificity was 94% (95% CI 92 to 96%). PPV was 16% (95% CI 6 to 31%) and NPV 99% (95% CI 98 to 100%). An abnormal ECG was more prevalent in patients with a true positive versus a false-positive troponin result (P=0.03). Conclusion: Troponin testing identified few cases of myocardial dysfunction. We found the test to have only 66% sensitivity. Troponin testing as a screen for myocardial injury in children has limited utility.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".