The importance of visualization of appendix on abdominal ultrasound for the diagnosis of appendicitis in children: A quality assessment review
Bibliographic record
Abstract
BACKGROUND: Ultrasound has the fi rst line investigation role in the diagnosis of acute appendicitis in children.The purpose of this study was to perform a quality assessment review on the visualization rate of appendix on ultrasound in children in the community hospital setting. METHODS:A retrospective chart review of the abdominal ultrasound findings for the visualization of the appendix was performed on paediatric patients ranging from 5 to 18 years.Data were collected from the two community hospitals of Toronto by using hospital electronic medical record for the ultrasound fi ndings in patients presented with abdominal pain. RESULTS:Data from two community hospitals indicated visualization rate of the appendix as 11.0% and 23.2% for site 1 and site 2 respectively.In cases where the ultrasound was repeated the visualization rate remains the same.A two-proportion z-test was performed to find whether the visualization of appendix increases the likelihood of diagnosing appendicitis.The results revealed that the visualization of an appendix (P=0.52),signifi cantly improved the diagnosis of appendicitis (z=34, P<0.001).CONCLUSION: Visualization of an appendix on ultrasound increases the likelihood of correctly diagnosing appendicitis.In our study, we found low visualization rate of appendix on ultrasound that could be the result of many factors that contribute towards the low visualization rate of an appendix on ultrasound.Hence, the challenges in identifying appendix should be minimized to improve the visualization and diagnosis of appendicitis on ultrasound.
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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 | 0.025 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".