Parenchymal Thoracic Splenosis: History and Nuclear Imaging Without Invasive Procedures May Provide Diagnosis
Bibliographic record
Abstract
UNLABELLED: Splenosis is a rare finding of ectopic splenic tissue found within the thoracic cavity, abdomen or peritoneal cavity. Most cases occur in the abdomen and the thoracic location is a comparatively rare finding. In thoracic splenosis the splenic tissue most often grows in the form of a nodule and the autotransplantation is usually caused by a previous operation and/or most commonly a penetrating or blunt trauma to the thoracoabdominal region, resulting in splenic rupture and in some cases left diaphragmatic tear. In majority of the cases the patients are asymptomatic and are incidentally diagnosed with left hemithorax pulmonary lesions found via chest radiography or thoracic computed tomography. We present a 45-year-old Caucasian male who was incidentally diagnosed with parenchymal thoracic splenosis secondary to a gunshot wound to the abdomen 13 years ago that resulted in distal pancreatectomy, splenectomy and gastrorrhaphy. In this case report we will briefly discuss the current updates in the literature regarding thoracic splenosis, and highlight the fact that the findings raise the suspicion of malignancy requiring numerous investigations yet early recognition of thoracic splenosis can prevent unnecessary tests and procedures. Preoperative diagnosis of splenosis should be made with the use of nuclear imaging studies such as the (99m)Tc heat-damaged erythrocyte study rather than computed tomography-guided biopsy or invasive surgery. KEYWORDS: Thoracic splenosis; Computed tomography; Ppancreatectomy; Splenectomy; Gastrorrhaphy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".