Clinical apparatus for the reduction of dose area product for patients undergoing x‐ray catheterization
Bibliographic record
Abstract
PURPOSE: The authors describe a design for prepatient region of interest attenuators (ROIAs) to reduce dose area product (DAP) for clinical use. The authors describe a model to predict DAP values from x-ray technique parameters recorded during a clinical procedure for image sequences obtained in the presence or absence of ROIAs. The model was developed primarily to determine what the DAP to a patient undergoing cardiac catheterization with a ROIA would have been if no ROIA had been used allowing a determination of DAP reduction. METHODS: Copper ROIAs with thicknesses that vary gradually so as not to cause significant image artifacts were constructed. X-ray image sequences were acquired on a clinical catheterization system with and without ROIAs with varying x-ray technique parameters. DAP values were measured for all said exposures using an ionization chamber and compared to a model the authors developed. RESULTS: The model can predict DAP values within 3.5% on average with or without ROIAs when compared to ionization chamber measurements. CONCLUSIONS: The proposed experimental design is adequate for measuring DAP reductions on the order of 1.5-3.5 that are expected when introducing a ROIA during patient catheterization imaging.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".