Diagnostic performance of chest radiography measurements for the assessment of cardiac chamber enlargement
Bibliographic record
Abstract
Background: The cardiothoracic ratio (CTR) is commonly assessed on chest radiography for detection of cardiac chamber enlargement, but the traditional cutpoint of 0.5 has low specificity. We sought to evaluate the diagnostic accuracy of new measurement techniques for the detection of cardiac enlargement on chest radiographs. Methods: We obtained retrospective cross-sectional data on consecutive patients who underwent both chest radiography and cardiac magnetic resonance imaging (MRI) within a 14-day interval between 2006 and 2016 at a large academic hospital network. We established the presence of cardiac chamber enlargement using cardiac MRI as the reference standard. We evaluated the diagnostic performance of different techniques for measuring heart size and CTR on frontal chest radiographs. Results: Of 152 patients included, 81 (53%) were men and the mean age was 52 years. Maximum heart diameter had the highest area under the receiver operating characteristic curve for detection of cardiac enlargement (0.827, 95% confidence interval 0.760–0.894). In the subgroup of posteroanterior chest radiography studies (n = 101), a CTR cutpoint of 0.50 had only moderate sensitivity (72%) and specificity (72%). In men, a maximum heart diameter cutpoint of 15 cm had a sensitivity of 86% and a negative likelihood ratio of 0.24, and a cutpoint of 19 cm had a specificity of 100% and a positive likelihood ratio of infinity. In women, a maximum heart diameter cutpoint of 13 cm had a sensitivity of 91% and a negative likelihood ratio of 0.15, and a cutpoint of 17 cm had a specificity of 91% and a positive likelihood ratio of 3.5. Interpretation: A traditional CTR cutpoint of 0.5 has limited diagnostic value. Simple heart diameter measurements have higher diagnostic performance measures than CTR.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".