Overuse or underuse of MRI scanners in private radiology centers in Tehran
Bibliographic record
Abstract
OBJECTIVES: The semiprivate health system in Iran has created an opportunity for unnecessary uses of advanced medical equipments including magnetic resonance imaging (MRI). This study aimed to evaluate the evidence for MRI overuse in private diagnostic imaging centers in Tehran, Iran. The objectives of this study were to determine the frequency of use of MRI scans for different complaints and to explore frequency of normal MRI findings as a function of unnecessary MRI use. METHODS: We conducted a survey among private MRI centers in Tehran, Iran, to study the proportion of MRI scans that may result in significant clinical finding. All MRI reports at a specific point in time at selected MRI centers were reviewed by a physician and the findings were recorded as normal, abnormal, or substantial changes. RESULTS: Of all the MRI reports, 17.2 percent had resulted in normal findings; 9.8 percent ordered for examination of headache, and 4.8 percent for lower back pain. CONCLUSION: Unnecessary MRIs are most likely to result in normal finding; although not all the MRI with normal results could be identified as unnecessary. Negative findings from MRI scans may be reassuring to both clinicians and patients. The proportion of normal findings in MRI scans did not provide evidence of MRI overuse in Iran. The results of this study warrant formation of guidelines for the use of MRIs for headache and low back pain disorders.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".