1089Prognostic value of a positive troponin across the age spectrum in over a quarter of a million patients (NHIC Troponin Study)
Bibliographic record
Abstract
Background: In the past two decades, assays for troponin have undergone vast improvements, allowing fast detection of troponin with increased precision. With improved sensitivity of current troponin assays, more patients end up with a positive troponin result. There is limited data to help inform the implications of a positive troponin test across the age spectrum, in clinical practice. Purpose: The aim of this study was to investigate the overall prognostic impact of a positive troponin result on all-cause mortality in patients in whom troponin testing has been done for clinical purposes. Methods: The NIHR Health Informatics Collaborative (NHIC) project was established to enable the sharing and repurposing of routinely captured clinical data for re-use in research. All troponin values measured during the study period (generally 2010 to 2017) were assembled from five contributing cardiovascular centres. The results were dichotomised as being positive or negative based on the 99th percentile of the upper limit of normal for all relevant troponin assays. All patients were followed up on the National Health Service Spine Application, Summary Care Record until death or censoring on 1st April 2017. Statistical analyses were performed using SPSS software version 24.0 (SPSS Inc., Chicago, Illinois, United States).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".