Improving sonographic visualisation of the appendix in a regional referral hospital
Bibliographic record
Abstract
Ultrasound is a first-line and often preferred imaging modality in the diagnosis of acute appendicitis. When the appendix is not visualised during a dedicated appendix ultrasound study, patients may require a CT study, which uses ionising radiation, or undergo conservative clinical observation with the inherent risk of clinical deterioration, perforation and sepsis. Median baseline data, at our hospital imaging department, revealed a rate of combined normal and abnormal appendix visualisation of 34.5% which is below the reported visualisation rates in the North American literature and well below the rates reported in the global literature. We embarked on a formal quality improvement (QI) project to improve the rates of appendix visualisation in our hospital ultrasound department. Using the Model of Improvement framework and a team approach, we generated and trialled multiple plan-do-study-act interventions over a project term of 12 months. In the second half of the project term, we saw a sustained rise in appendix visualisation exceeding our original stretch goal of 75% visualisation which was sustained 6 months after the formal project end (p<0.001). This rise was accompanied by a commensurate increase in sonographer confidence in appendix visualisation. In our case, the Model of Improvement methodology proved successful in solving our complex problem of sonographic appendix under-visualisation. The learnings of this QI project have been widely shared and spread according to the ethos of QI.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".