Assessing Clinical Appropriateness: A retrospective audit of CTEs performed in St. John’s, Newfoundland and Labrador, during 2022.
Bibliographic record
Abstract
Objectives: Computed tomography enterography (CTE) is an abdominopelvic computed tomography specifically designed to evaluate the small bowel. Guidelines exist regarding what clinical situations warrant CTE. This study intends to assess how CTE is currently used locally in St. John’s, Newfoundland and Labrador (NL) and compare the results to the literature to determine whether CTEs are being requested following best practices. Such results will inform referring healthcare providers as to whether they are requesting CTE appropriately. Methods: This study consisted of a retrospective audit of all CTEs (422) performed in St. John’s, NL in 2022. Extracted information included the patient’s clinical history, age and sex, and results of the imaging study. Results: 114 studies were requested to investigate iron deficiency anemia, of which 92 did not identify a lesion to account for the patient’s symptoms; 13 showed small bowel angiodysplasia. 92 studies were ordered to investigate the small bowel in patients with known Crohn’s diagnosis; 56 identified findings of active disease. 79 studies were ordered to investigate suspected small bowel inflammatory disease; 56 demonstrated no evidence of the same. 28 studies were ordered to investigate vascular symptomatology, of which 27 did not localize a small bowel cause. Conclusions: CTE is, undoubtably, a valuable imaging modality to visualize the small bowel. Most of the clinical indications prompting CTE requests within the St. John's region aligned with published indications for CTE referral. However, the majority of studies did not identify a small bowel cause for the patient’s symptoms.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".