Using logistic regression models to investigate the effects of high-sensitivity cardiac troponin T confounders on ruling in acute myocardial infarction
Bibliographic record
Abstract
OBJECTIVES: Confounding factors, including sex, age, and renal dysfunction, affect high-sensitivity cardiac troponin T (hs-cTnT) concentrations and the acute myocardial infarction (AMI) diagnosis. This study assessed the effects of these confounders through logistic regression models and evaluated the diagnostic performance of an optimized, integrated prediction model. METHODS: This retrospective study included a primary derivation cohort of 18,022 emergency department (ED) patients at a US medical center and a validation cohort of 890 ED patients at a Canadian medical center. Hs-cTnT was measured with 0/3 h sampling. The primary outcome was index AMI diagnosis. Logistic regression models were optimized to predict AMI using delta hs-cTnT and its confounders as covariates. The diagnostic performance of model cutoffs was compared to that of the hs-cTnT delta thresholds. Serial logistic regressions were carried out to evaluate the relationship between covariates. RESULTS: The area under the curve of the best-fitted model was 0.95. The model achieved a 90.0% diagnostic accuracy in the validation cohort. The optimal model cutoff yielded comparable performance (90.5% accuracy) to the optimal sex-specific delta thresholds (90.3% accuracy), with 95.8% agreement between the two diagnostic methods. Serial logistic regressions revealed that delta hs-cTnT played a more predominant role in AMI prediction than its confounders, among which sex is more predictive of AMI (total effect coefficient 1.04) than age (total effect coefficient 0.05) and eGFR (total effect coefficient -0.008). CONCLUSIONS: The integrated prediction model incorporating confounding factors does not outperform hs-cTnT delta thresholds. Sex-specific hs-cTnT delta thresholds remain to provide the highest diagnostic accuracy.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| 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".