CAR/CETARS/CSAR Practice Guideline on Imaging the Adult Patient With Right Lower Quadrant Pain
Bibliographic record
Abstract
In 2023, the Canadian Society of Abdominal Radiology (CSAR) and Canadian Emergency, Trauma, and Acute Care Radiology Society (CETARS) received Canadian Association of Radiologists (CAR) member feedback that there was an unmet educational need for guidance in the imaging investigation of right lower quadrant (RLQ) pain. Members requested specific guidance on how to handle controversial scenarios including which test to order when, specifics of imaging protocols, and managing pregnant patients who have RLQ pain-all from a Canadian perspective. After conducting an exhaustive literature review, the working group agreed that a Canadian-specific set of guidelines was warranted. The management recommendations presented in this guideline were discussed as a group to achieve expert consensus. As the workup for RLQ pain can vary considerably in the paediatric population, the scope of this paper was restricted to adults (18 years of age or older). Whenever possible, the best evidence was used to inform the clinical guidance, and where gaps existed, the guidelines reflect consensus among experts in the field. The result is a framework to aid in this process of managing patients with RLQ pain across various clinical scenarios while addressing current questions and controversies, particularly those most relevant to the Canadian healthcare system.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".