Asymptomatic thoracic splenosis after thoracoabdominal trauma: establishing a diagnosis
Bibliographic record
Abstract
A 58-year-old male was referred for evaluation ofmultiple pulmonary nodules suspicious for malig-nancy. Thirty-five years earlier, he had beeninvolved in a motor vehicle crash and was diagnosedwith a fractured mandible, multiple left-sided ribfractures, a pulmonary laceration with left haemo-pneumothorax, ruptured left diaphragm with intra-thoracic stomach and splenic laceration. Afterinitial assessment and stabilization he underwentsplenectomy and repair of the left diaphragmaticlaceration. He recovered uneventfully from theseinjuriesandwasdischarged.Recently,hedevelopedan upper respiratory tract infection and received achest radiograph (CXR) as part of his investigations.Apical and basal pleural-based nodules were iden-tified CXR (Fig. 1) and further clarified by computedtomography (CT) scan of the chest (Fig. 2).Attempted image guided trans-thoracic aspirationof these lesions in a peripheral hospital was abortedbecause of fear of complications. Video assistedthoracoscopic surgery (VATS) biopsy of the lesionswas performed. Intra-operative frozen section con-
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".