ESTIMATION OF ENTRANCE SURFACE AIR KERMA IN DIGITAL RADIOGRAPHIC EXAMINATIONS
Bibliographic record
Abstract
PURPOSE: Contribution of radiation doses from medical X-ray examination to collective dose is significant. Unusually, high doses may increase the risk of stochastic effects of radiations. Therefore, radiation dose assessment was performed in 241 digital X-ray examinations in the study and was compared with published dose reference levels (DRLs). METHODS: Entrance surface air kerma (ESAK) was calculated in chest PA, cervical AP/Lat, abdomen AP, lumbar AP/Lat and pelvis AP digital radiographic examinations (119 male and 122 female) following the International Atomic Energy Agency recommended protocol. Initially, 270 digital examinations were selected, reject analysis was performed and final 241 examinations were enrolled in the study for dose calculations. The exposure parameters and X-ray tube output were used for dose calculations. Effective doses were estimated with the help of conversion coefficients from ICRP 103. RESULTS: Median ESAK (mGy) and associated effective doses obtained were cervical spine AP (1.30 mGy, 0.045 mSv), cervical spine Lat (0.25 mGy, 0.005 mSv), chest PA (0.11 mGy, 0.014 mSv), abdomen AP (0.90 mGy, 0.118 mSv), lumbar spine AP (1.52 mGy, 0.177 mSv), lumbar spine Lat (7.76 mGy, 0.209 mSv) and pelvis AP (0.82 mGy, 0.081 mSv). Results were compared with the studies of UK, Oman, India and Canada. CONCLUSION: The calculated ESAK and effective dose values were less than or close to previously published literature except for cervical spine AP and lumbar spine Lat. The results reinforce the need for radiation protection optimization, improving examination techniques and appropriate use of automatic exposure control in digital radiography. ESAK values reported in this study could further contribute to establishing local DRLs, regional DRLs and national DRLs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".