First implementation of quality control procedures on selected X-ray machines in South of Benin
Bibliographic record
Abstract
Abstract Introduction: The use of X-ray equipment for medical diagnostic radiography procedures has increased due to advances and complexity of radiological procedures. Achieving good image quality while keeping exposure of workers, public and patient exposure to an acceptable level has become a prerequisite for the radiology department in order to comply with best international practices. The aim of this study was to undertake quality control measurement of seven (7) diagnostic radiography equipment in the south of Benin, the first of its kind. Material and methods: Multifunction detector (Piranha) and beam alignment test tool were used to perform quality control tests on seven (7) X-ray units. The method used as well as the interpretation of the results was based on the American Association of Physicists in Medicine (AAPM), United States Food and Drug Administration (FDA), Healing Arts Radiation Protection (HARP), Institute of Physics and Engineering in Medicine (IPEM), International Atomic Energy Agency (IAEA) and Canadian Safety code 35 (S.C 35) recommendations. Results: The quality control results showed that all X-ray equipment investigated were within standard limits for accuracy of exposure time below 10 ms; reproducibility of kVp, exposure time and dose output; specific dose-kVp 2 linearity; and specific dose-mAs linearity. Five (5) out of seven (7) diagnostic X-ray machines passed quality control tests such as X-ray beam alignment, exposure time above 10 ms and kVp accuracy. One (1) X-ray machine failed the quality control test of beam filtration at 70 kVp and above. Conclusions: The findings of this study have provided baseline data for other radiology departments to embark on similar QA/QC activities, and also explore options for optimization of patient dose. However, there is a need to extend the study to cover more diagnostic X-ray machines throughout the country. It is anticipated that this would ultimately assist in improving radiation protection and safety during medical diagnostic radiological procedures.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".