How Well Are We Respecting Patient Privacy in Medical Imaging? Lessons Learnt from a Departmental Audit
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Preservation of patient privacy and dignity are basic requirements for all patients visiting a hospital. The purpose of this study was to perform an audit of patients' satisfaction with privacy whilst in the Department of Medical Imaging (MI) at the Civic Campus of the Ottawa Hospital. MATERIALS AND METHODS: Outpatients who underwent magnetic resonance imaging (MRI), computed tomography (CT), ultrasonography (US), and plain film (XR) examinations were provided with a survey on patient privacy. The survey asked participants to rank (on a 6-point scale ranging from 6 = excellent to 1 = no privacy) whether their privacy was respected in 5 key locations within the Department of MI. The survey was conducted over a consecutive 5-day period. RESULTS: A total of 502 surveys were completed. The survey response rate for each imaging modality was: 55% MRI, 42% CT, 45% US, and 47% XR. For each imaging modality, the total percentage of privacy scores greater than or equal to 5 were: 98% MRI, 96% CT, 94% US, and 92% XR. Privacy ratings for the MRI reception and waiting room areas were significantly higher in comparison to the other imaging modalities (P = .0025 and P = .0227, respectively). CONCLUSION: Overall, patient privacy was well respected within the Department of MI.
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 | 0.067 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".