Determination of the Optimal Echocardiographic Scoring System to Quantify Carcinoid Heart Disease
Bibliographic record
Abstract
BACKGROUND: Carcinoid heart disease (CHD) is an important complication of metastatic neuroendocrine disease, requiring regular monitoring to enable intervention prior to right heart failure. We aimed to identify the most appropriate echocardiographic scoring systems for the quantitative assessment of CHD. METHODS: In this prospective study conducted between April and October 2012 in two European Neuroendocrine Tumor Society (ENETS) Centres of Excellence, patients with neuroendocrine tumours with liver metastases and/or carcinoid syndrome underwent transthoracic echocardiography and blood sampling for serum N-terminal pro-brain natriuretic peptide (NT-proBNP) and plasma 5-hydroxyindoleacetic acid (5-HIAA). Each patient was assessed according to six echocardiographic scoring systems. The individual scoring systems' feasibility, observer variability, sensitivity, specificity and correlation with the concentration biomarkers were determined. RESULTS: 100 patients were included; 21% had echocardiographic evidence of CHD. All scores discriminated highly between those with/without CHD, with no single score performing significantly better than another. The severity, determined using all of the scoring systems, correlated with the concentration of both biomarkers, but the strongest correlations were seen between the Bhattacharyya score and serum NT-proBNP. CONCLUSION: All scoring systems are comparable in terms of sensitivity and specificity for the detection of CHD. There is a variation in the feasibility of the scoring systems due to varying complexity of the score components. All scores correlate with NT-proBNP and plasma 5-HIAA. The Westberg score appears to be the most optimal scoring system for use in screening of CHD whereas the more complex scoring systems are more suited to the patient with established disease who may require surgical intervention.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".