Frequency of Visualization and Thickness of Normal Appendix at Nonenhanced Helical CT
Bibliographic record
Abstract
PURPOSE: To evaluate the frequency of visualization, thickness, and features of the normal appendix at nonenhanced helical computed tomography (CT). MATERIALS AND METHODS: Three radiologists blinded to patient surgical history retrospectively reviewed CT scans obtained for renal colic assessment in 187 consecutive patients. No contrast material was administered. The frequency of visualization and the two-wall thickness of normal appendices were recorded. Interobserver agreement and effect of adequacy of intraperitoneal fat on identification of the appendix were assessed. RESULTS: The prevalence of appendectomy was 10.7% (20 of 187 patients). The means for the three reviewers' sensitivity, specificity, positive and negative predictive values, and accuracy of visualization of normal appendix were 79% (CI: 73%, 84%), 90% (CI: 78%, 96%), 98% (CI: 97%, 99%), 34% (CI: 22%, 47%), and 80% (CI: 74%, 86%), respectively. There was no significant difference among the three reviewers (P >.05) according to conditional logistic regression and exact McNemar test results. For all reviewers, the frequency of appendix visualization was significantly lower in patients with less intraperitoneal fat (P =.01-.001, chi(2) test). The mean thickness of normal appendix if no intraluminal content was visualized was 6.6 mm +/- 1.0 (SD), and the mean thickness, excluding visualized intraluminal content, was 3.6 mm +/- 0.8. The nonweighted kappa value for interobserver agreement for normal appendix visualization was 0.69-0.75 among the three reviewers, which indicated good to excellent agreement. CONCLUSION: Most normal appendices are seen at nonenhanced helical CT. The thickness of normal appendix, when the content is not recognizable, overlaps the values currently used to diagnose appendicitis at CT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".