The diagnosis of primary small intestinal lymphoma by multislice spiral computed tomography
Bibliographic record
Abstract
Objective To investigate the manifestation and diagnostic value of multislice spiral CT (MSCT) in primary small intestinal lymphoma (PSIL). Methods The MSCT data of 32 operated cases of PSIL diagnosed pathologically and in accordance with Dawson criteria were retrospectively analyzed. Results Of these 32 patients with PSIL, 30 had solitary lesion (23 lesions located in the ileum, 6 in the jejunum, and 1 in the duodenum), and 2 were found in both the ileum and the jejunum. PSIL patients could be categorized into 5 types according to CT manifestation: infiltration type (n=12), luminal aneurismal dilatation type (n=10), polypoid mass type (n=2), mesentery type (n=3) and mixed type(n=5). The MSCT manifestations were: mucosa of the involved bowel was continous, smooth and intact; irregular thickening of bowel wall mainly involved submucosa and muscular layer; involved bowel still kept some degree of distensibility and fragility. The lesion showed mild to moderate intensification. All five types of PSIL did not show significant difference in CT value before and after enhancement. MSCT enteroclysis was done in 20 cases, its diagnostic accuracy in localization and characterization was 100% and 95% respectively. Conclusions MSCT manifestation of PSIL was characteristic; MSCT enteroclysis was of high value for the diagnosis of PSIL before surgical operation.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".