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 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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".