Public or Private Magnetic Resonance Imaging: What Do the Patients Think?
Bibliographic record
Abstract
PURPOSE: We described the demographic, clinical, and attitudinal profiles of patients awaiting magnetic resonance imaging (MRI) at a private and at a hospital-based facility, and hypothesized that they would not differ significantly. METHODS: A survey of patients attending a hospital facility and a privately owned venue in an Ontario city. Descriptive, bivariate, and logistic regression analyses were performed. RESULTS: A total of 314 patients provided data, with a higher response rate at the private clinic than at the hospital-based clinic (97% vs 60%). For the majority of patients (58%), the MRI was scheduled to follow up known disease; 55.8% waited more than 4 weeks; 6.4% waited more than 6 months. One-third of patients expressed a willingness to travel to the United States and pay for the MRI, 41% expressed a willingness to pay within Ontario, and 66% were willing to travel elsewhere in Ontario. They were more likely to be at the hospital-based MRI if they were being followed up for known disease and had a diagnosis of cancer, whereas those patients at the private MRI facility reported significantly more pain; 59% of the hospital-based sample and 72% of the private clinic sample reported significantly reduced quality of life because of their health problem. DISCUSSION: These data provide interesting insights into the characteristics of patients awaiting an MRI and the attitudes of patients towards public and private MRI clinics. There were significant attitudinal differences between those patients attending the 2 facilities. Pain, coupled with a long wait, may create an incentive for patients to conclude that private clinics should be permitted if the hospital environment is unable to improve access times.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".