Comparison of dual energy subtraction chest radiography and traditional chest X-rays in the detection of pulmonary nodules.
Bibliographic record
Abstract
BACKGROUND: Dual energy subtraction (DES) radiography is a powerful but underutilized technique which aims to improve the diagnostic value of an X-ray by separating soft tissue from bones, producing two different images. Compared to traditional chest X-rays, DES requires exposure to higher doses of radiation but may achieve higher accuracy. The objective of this study was to assess the clinical benefits of DES radiography by comparing the speed and accuracy of diagnosis of pulmonary nodules with DES versus traditional chest X-rays. METHODS: Five radiologists and five radiology residents read the DES and traditional chest X-rays of 51 patients, 34 with pulmonary nodules and 17 without. Their accuracy and speed in the detection of nodules were measured using specialized image display software. RESULTS: DES radiography reduced reading time from 13 to 10 sec (P<0.0001) in staff and from 21 to 15 sec in residents (P<0.0001). There was also a small increase in sensitivity 0.58 to 0.67 overall (P<0.10) with no change in specificity (0.85 overall). CONCLUSIONS: By eliminating rib shadows in soft tissue images, DES improved the speed and accuracy of radiologists in the diagnosis of pulmonary nodules.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".