CAPABILITIES OF COMPUTED TOMOGRAPHY TO EVALUATE POLYMORPHIC CHANGES IN DESTRUCTIVE PANCREATITIS
Bibliographic record
Abstract
Objective: to determine whether the lower-density pancreatic parenchymal areas detected by a computed tomography (CT) study in patients with acute pancreatitis correspond to the necrotic portions of the gland or whether these changes may be reversal.Material and methods. The investigation covered 25 patients who had undergone or dynamic CT studies made at different time intervals. Two independent investigators with 4 and 19 years of experience retrospectively analyzed the results of both CT studies. Target estimation was made of the extent (volume) of and CT density changes in the hypodense areas of the gland parenchyma.Results. Seven (28%) of the 25 patients were noted to have higher CT density in the areas that had decreased density during primary CT studies (more than a 30 HU increase was rated as significant). There was a statistically significant difference between the patient groups when comparing the extent of hypodense areas and the difference in CT density (t-test, p=0.006); Mann–Whitney U-test, p=0.01 for extent difference and t-test, p=0.00; Mann–Whitney U-test, p=0.00 for CT density difference. There was also a correlation between the extent of hypodense areas and the difference in their CT density (Pearson: r=-0.533, p=0.006; Spearman: r=-0.636, p=0.001).Conclusion. The results of our investigation may suggest that the lower-density pancreatic parenchymal areas cannot always correspond to necrotic changes and may be reversible.
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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 | 0.002 | 0.012 |
| 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 | 0.001 | 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".