The Mysterious Organ. Spectrum of Focal Lesions within the Splenic Parenchyma: Cross-Sectional Imaging with Emphasis on Magnetic Resonance Imaging
Bibliographic record
Abstract
Incidental splenic lesions are frequently encountered at imaging performed for unrelated causes. Splenic cysts, hemangiomas, and lymphomatous involvement are the most frequently encountered entities. Computed tomography and sonography are commonly used for initial evaluation with magnetic resonance imaging reserved as a useful problem-solving tool for characterizing atypical and uncommon lesions. The value of magnetic resonance imaging lies in classifying these lesions as either benign or malignant by virtue of their signal-intensity characteristics on T1- and T2-weighted imaging and optimal depiction of internal hemorrhage. Dynamic contrast-enhanced sequences may improve the evaluation of focal splenic lesions and allow characterization of cysts, smaller hemangiomas, and hamartomas. Any atypical or unexplained imaging feature related to an incidental splenic lesion requires additional evaluation and/or follow-up. Occasionally, biopsy or splenectomy may be required for definitive assessment given that some of tumours may demonstrate uncertain biologic behavior.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".