Method for the evaluation of a average glandular dose in mammography
Bibliographic record
Abstract
This paper concerns a method for accurate evaluation of average glandular dose (AGD) in mammography. At different energies, the interactions of photons with tissue are not uniform. Thus, optimal accuracy in the estimation of AGD is achievable when the evaluation is carried out using the normalized glandular dose values, g(x,E), that are determined for each (monoenergetic) x-ray photon energy, E, compressed breast thickness (CBT), x, breast glandular composition, and data on photon energy distribution of the exact x-ray beam used in breast imaging. A generalized model for the values of g(x,E) that is for any arbitrary CBT ranging from 2 to 9 cm (with values that are not whole numbers inclusive, say, 4.2 cm) was developed. Along with other dosimetry formulations, this was integrated into a computer software program, GDOSE. FOR, that was developed for the evaluation of AGD received from any x-ray tube/equipment (irrespective of target-filter combination) of up to 50 kVp. Results are presented which show that the implementation of GDOSE. FOR yields values of normalized glandular dose that are in good agreement with values obtained from methodologies reported earlier in the literature. With the availability of a portable device for real-time acquisition of spectra, the model and computer software reported in this work provide for the routine evaluation of AGD received by a specific woman of known age and CBT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".