Limited Utility of Screening Electrocardiograms in Systemic Sclerosis: Data from the Canadian Scleroderma Research Group
Bibliographic record
Abstract
OBJECTIVE: Recommendations have been made to use electrocardiograms (EKGs) to screen for cardiac disease in systemic sclerosis (SSc). The objective of this study was to compare the prevalence of EKG abnormalities in SSc and controls to help determine if the EKG should be used as a screening tool. METHODS: EKGs from patients with SSc were compared with those from a random sample of age- and gender-matched controls. Two cardiologists read all EKGs using a standardized approach. The groups were compared using t-tests, chi-squared tests, and Fisher exact tests. RESULTS: Patients with SSc (n = 833, mean ± SD disease duration 11.3 ± 9.3 years; 39.4% had diffuse cutaneous SSc) and controls (n = 832) were included. The prevalence of conduction and rhythm abnormalities were similar in the SSc and control groups. More patients with SSc than controls had possible right atrial enlargement (5% vs 0.1%, P < 0.001), right axis deviation (3.2% vs 0.4%, P < 0.001), left atrial enlargement (9.2% vs 1.6%, P < 0.001), poor/abnormal R progression (5.6% vs 2.2%, P < 0.001) and nonspecific T wave abnormalities (6.1% vs 3.4%, P = 0.008). CONCLUSION: Our findings suggest that conduction abnormalities are not more prevalent in those with SSc than in controls. Evidence of right heart stress on EKG in SSc may be secondary to pulmonary hypertension and left atrial enlargement, and poor R wave progression in precordial leads may indicate myocardial damage. Future studies are required to determine if these EKG abnormalities represent underlying structural heart disease, and, until that is proven, EKGs should not be considered a screening tool for cardiac abnormalities in SSc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".