Factors affecting perception of the normal pediatric appendix on sonography
Bibliographic record
Abstract
BACKGROUND: To determine if an inherent perception skill along with sonographer experience, knowledge base, scanning time play a role in the identification of the normal appendix in the pediatric population. This is a retrospective review of pediatric (< 18 years old) patients with a clinical suspicion of acute appendicitis presenting to the emergency department of two affiliated academic tertiary care hospitals over a 1-year time span. All patients had a sonogram performed by 1/15 sonographers or by 1/8 on-call radiology residents. Those with a normal or non-visualized appendix with subsequent discharge from ER were included in the study. Patient demographics, minutes spent scanning, and sonographer years of experience in general abdominal scanning and residents level of training were recorded. RESULTS: Of the 127 patients included in the study, 51 (40%) were male and 76 (60%) were female, with a mean age of 11.8 ± 4.2 years. Sonographers who failed to see a normal appendix had less experience (median 8 years) than those who did visualize the appendix (median 15 years), p ≤ 0.001. Longer time spent scanning was also associated with visualizing a normal appendix (20.4 versus 29.1 min, p = 0.001). In multivariable logistic regression, more time spent scanning (OR 1.04, 95% CI 1.01, 1.07, p = 0.012) and increased sonographer experience (OR 1.07, 95% CI 1.02, 1.13, p = 0.012) resulted in greater odds of perceiving the appendix. The top 4 were significantly more likely to visualize the appendix (88.0%) than all of the other combined (20.8%, p < 0.001), and they also had substantially more experience (median 15 years versus 8 years, p < 0.001). Overall, sonographers were more likely to see a normal appendix (61%) than the residents (14%), p < 0.001. CONCLUSION: Sonography to rule out appendicitis in the pediatric patient is in general most successful when performed by experienced sonographers with adequate time to perform the scan. Triaging patients to those sonographers who have displayed optimal perceptual ability of the normal appendix may help optimize patient care and hospital resources. Having experienced sonographers available after hours would allow for optimal care in the setting of 'query' appendicitis.
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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 | 0.001 | 0.007 |
| 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) | 0.002 | 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".