The use of magnetic resonance imaging in acute knee injuries can be reduced by non-physician expert clinics
Bibliographic record
Abstract
INTRODUCTION: The routine use of magnetic resonance imaging (MRI) for the assessment of acute knee injuries is controversial. The goal of this study is to present an audit of patients seen in a dedicated Acute Knee Injury Clinic (AKIC) to determine the frequency and appropriateness of MRI utilization. METHODS: A retrospective review identified all patients who had an MRI and a randomly selected control group without MRI. The MRI was classified based on whether it was ordered by the AKIC team or by an external clinician. The consensus-based 'Indications for Urgent MRI in Acute Soft Tissue Knee Problems' were applied to both groups. An MRI was considered appropriate if any of the indications were met. RESULTS: The overall MRI utilization rate was 23% (142/611). Of the MRIs performed, 32% (46/142) met the indications. About 94% (33/35) of the MRIs ordered by the AKIC experts met the indications, compared to only 12% (13/107) of those ordered externally. No patients in the control group met the indications. Diagnoses were similar between groups. DISCUSSION: These results suggest that application of guidelines by experts in knee evaluation can significantly reduce expensive MRI utilization in patients with acute knee injuries without negatively impacting the appropriate diagnosis and disposition.
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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 | 0.003 | 0.030 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".