The Imaging Requisition : The U2018No Manu2019S Landu2019 of Radiology and Emergency Medicine
Bibliographic record
Abstract
Purpose: The information provided on imaging requisitions is often a point of contention. This study aims to assess the quality of information emergency physicians are writing on requisitions for ultrasound and computed tomography imaging, and to assess if information provided, or lack thereof, may impact study selection and protocol. Methods: 50 CT scans and 50 ultrasound scans from October u2013 November 2016 were randomly selected from a central emergency medicine database. An excel database was created documenting criteria from the emergency physician clinical encounter sheet including: symptoms, signs, lab work, past medical and surgical history, prior imaging, and a clinical question. The database also included the verbatim information written on each imaging requisition sheet. A staff radiologist with greater than 15 years of experience and a third year radiology resident independently reviewed and graded each imaging requisition sheet history using an agreed upon grading system as follows; grade 1: inadequate information to correctly select and protocol study, grade 2: adequate information to select study type but more information preferred to guide correct protocolling, grade 3: good clinical information provided as per Canadian Association of Radiology (CAR) best practice guidelines for study selection and protocol. The reviewers would then compare each requisition sheet to the ER clinical encounter information and document whether the added information would have altered their imaging protocol. Results: The resident reviewer rated 28% of requisitions as grade 3, while the staff radiologist rated 16% as grade 3. 69% and 80% of the requisitions were rated as grade 2 by the resident and staff, respectively, and only 3% and 4% were rated as grade 1. The staff radiologist identified 14 studies that would have an altered protocol with a complete clinical picture, and the resident identified 11, 5 of which overlapped with the staffu2019s choice of study. Conclusions: Overall, 96.5% of imaging requisitions contain at least adequate clinical information for a radiologist to glean enough information to direct their study selection but drops to 86% for optimization of study protocol. The quality of information provided on CT and ultrasound imaging requisitions by emergency physicians is satisfactory for imaging study selection but optimization of protocols would benefit from a more complete clinical picture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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; both teacher heads agree on what is shown here.
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".