Abstract 19205: Most Hospitalized Patients With Elevated Cardiac Troponin Levels Die From Non Cardiac Causes
Bibliographic record
Abstract
Cardiac troponins (Tn) have major prognostic importance as bio-markers in acute coronary syndromes (ACS). However during hospitalizations, Tn levels are often assayed in patients who do not have an acute coronary syndrome. To examine the prognostic implications of elevated Tn levels in patients with and without cardiovascular disease, survival at 12 months among 251,944 patients admitted in the period 2006-8 to public hospitals in NSW, Australia, was linked to measured troponin levels and their ICD-109 diagnostic code. In total 171,253 patients (68%) had a cardiovascular ICD code (CVD100-199) and 80,691 (32%) had a non-CVD code. Troponin positivity was defined as ≥ 1X the upper reference limit (URL) for the troponin T or I assay used at each institution. In total, of troponin positive patients at 12 months, 3,120 (33%) who had a CVD code had died whereas 6,205 (67%) of patients with a non CVD code had died. The hazard ratios for mortality among patients who were ‘troponin positivity’ and had non-CVD codes, adjusted for age and gender, were 2.0 [1.09-2.01] and for CVD codes 100-199 was 2.5 [2.3-2.7]. Among troponin positive patients with non-CVD admission diagnoses, the three most frequent causes of death at 12 months were: 1) diseases of the respiratory system, and 2) neoplasia, and 3) injury, poisoning and or external causes. In conclusion, among a large cohort of patients admitted over a two year period to public hospitals in NSW, Australia, more troponin positive patients with a non-CVD ICD code died at 12 months than those with an CVD code for their admission. Non cardiac disease is a common cause of elevated troponin levels and it has significant adverse prognostic consequences.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".