Focused Abdominal Computed Tomography in Clinically Suspected Adolescent Acute Appendicitis
Bibliographic record
Abstract
Background: Acute appendicitis (AA) is one of the most common causes of acute non-traumatic abdominal pain. The use of computed tomography (CT) in facilitating the diagnosis of AA in patients above the age of 50 where right iliac fossa (RIF) pain may represent a wide spectrum of differentials is well established. However, few studies have explored the value of low radiation, unenhanced focused abdominal computed tomography (FACT) in adolescent patients where AA represents the main differential for RIF pain. In this study, we aimed to examine the diagnostic accuracy of FACT scan in diagnosing AA in adolescent patients at a tertiary teaching children ’s hospital. Methods: The study was based on cross-sectional design and occurred over a 6-month period between November 2008 and May 2009. A prospective cohort of 95 patients between the ages of 10 - 21 years with clinically suspected AA satisfied the inclusion criteria. Patients with recent abdominal surgery or those who were pregnant were excluded from the study. The necessary institutional ethical approval was granted prior to study commencement. Results: There was slight male preponderance of 51 patients (54%) and 44 females (46%). The mean age of AA was 12.75 ± 2.7 years (range 10 - 21). Statistical analysis showed FACT scan was 97.32% sensitive, 88.42% specific, with a positive predictive value of 98.8% and a negative predictive value of 80.0% in diagnosis of AA. The overall diagnostic accuracy of FACT scan in our study was 96.8%. Conclusions: Unenhanced FACT scan is rapid, cost-effective and safe in diagnosis of adolescent AA. J Curr Surg. 2018;8(1-2):7-12 doi: https://doi.org/10.14740/jcs339w
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".