Computed tomography with clinical scoring to differentiate phytobezoar from feces in childhood small bowel obstruction
Bibliographic record
Abstract
BACKGROUND: Identification of phytobezoar in childhood small bowel obstruction (SBO) characterized by smallbowel feces sign (SBFS) is still challenging. The aim of our study was to assess the diagnostic performance of quantitative computed tomography (CT) analysis combined with the Acute General Emergency Surgical Severity-Small Bowel Obstruction (AGESS-SBO) scoring system in determining phytobezoar-related SBO. METHODS: Sixteen phytobezoar-related SBO were categorized as the phytobezoar group and the other 19 SBFSpositive SBO was categorized as the control group. Demographic data, clinical presentation, and laboratory and CT findings were collected and analyzed. Each patient`s AGESS-SBO score was determined according to the individual medical record. Multivariate logistic regression analyses were used to identify significant variables associated with phytobezoar-related SBO. Diagnostic performance of key variables was assessed using receiver operating characteristic (ROC) curve analysis. RESULTS: Compared to the control group, the phytobezoar group showed a significantly shorter debris maximal length (3.0 ± 0.5 cm vs. 3.5 ± 0.7 cm, P < 0.05), stronger attenuation (12.6 ± 5.9 HU vs. 8.2 ± 4.0 HU, P < 0.05) in CT, and higher AGESS-SBO scores (4.5 [interquartile (IQR): 4-5]) vs. (2 [IQR: 1-4]). With the combination of debris attenuation (with a cut-off of > 9.0 HU) and AGESS-SBO score (with a cut-off of > 3 points), the positive predictive value (PPV) and negative predictive value (NPV) to diagnose phytobezoar-related SBO were 80% (12/15) and 84% (16/19), respectively. CONCLUSIONS: The diagnostic method of integrating quantitative CT analysis and the AGESS-SBO scoring system can improve the identification accuracy of phytobezoar in SBFS-positive childhood SBO.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".