Bibliographic record
Abstract
Background:Cardiac evaluation is an important part of the liver transplantation work up to achieve higher post-transplant survival rates.Identification of Coronary Artery Disease (CAD) among asymptomatic candidates is done by cardiac stress tests & Transthoracic ECHO.Cardiac catheterization is considered to be the gold standard in investigating for the presence and extent of CAD.Methods:We investigated the role of Cardiac Calcium Score (CCS) to predict the presence of CAD among patients with major cardiac risk factors including DM, HTN and smoking.Following IRB approval, medical records of patients evaluated at Johns Hopkins Liver Transplant Program, between 1/1/2011 & 5/1/2013 were retrospectively reviewed.Results:CCS was obtained on 66 patients out of 175, after discussing with consultant cardiologist on the liver transplant team.There were 40 males and 26 females.Mean age was 57.8 ± 0.7 years.Mean MELD was 15.2 ± 6.8.Mean CCS was 379.6 ± 639.8.Cardiac cath was done by a single experienced interventional cardiologist on 16 patients.Four patients had coronary obstruction of <50%, 12 patients had maximum obstruction of ≥50% (range 50-90%).Mean CCS was 750.3 ± 714 in patients with <49% stenosis and 883.8 ± 659.7 in patients with ≥50% stenosis.There was no statistical difference between the groups (P > 0.05).The ROC analysis revealed 91% sensitivity and 50% specificity at cutoff of 243 for the identification of occult CAD.Conclusion:Early identification of CAD is important to be able to achieve better post-transplant outcomes.Currently used Stress tests are known to have a lower sensitivity but a higher specificity.CCS above 250 can be considered to select which patients will benefit from further investigation with a cardiac cath.This may eventually eliminate unnecessary procedures in high risk patients with end stage liver disease.More data is needed to verify these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".