A comparison of Saudi Arabian and Australian radiographers' perceptions of computed tomography radiation dose
Bibliographic record
Abstract
Abstract Background: Computed tomography (CT) is a powerful diagnostic tool, but the radiation delivered to paediatric patients needs to be kept to a minimum. Thus, CT education and protocols must be continuously reviewed, particularly with respect to paediatric CT examinations. Purpose: To investigate the knowledge and perceptions of paediatric CT radiographers from Australia and Saudi Arabia regarding paediatric CT dose. Methods: Interviews were conducted with CT radiographers working in dedicated paediatric hospitals during 2010. Their training and perceptions of paediatric CT radiation dose were evaluated, along with their departments' policies. Actual dose measurements from their departments were compared to their perceptions to reveal radiographers' awareness of their department's typical dose relative to those delivered by their contemporaries. Results: Almost all surveyed radiographers were willing to minimise their radiation dose, but many Saudi Arabian radiographers were not allowed to make any changes to their CT protocols. CT protocols in Saudi Arabia were wholly defined by the CT machine vendors' recommendations, whereas most Australian CT protocols were established following reviews of the appropriate literature. Conclusion: Australian and Saudi Arabian CT radiographers' training and perceptions of paediatric CT dose vary substantially, and differences exist in terms of workplace culture. Continuous professional development will assist Saudi Arabian radiographers to reduce CT radiation dose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".