The Risk of Misdiagnosing the Primary Site Responsible for Bone Metastases in Patients With Chronic Lymphocytic Leukemia and a Second Primary Carcinoma
Bibliographic record
Abstract
Chronic lymphocytic leukemia (CLL) is a common malignancy which may coexist with other primary cancers. CLL is rarely the cause of solitary bone lesions; such lesions in the context of CLL are believed to result from either Richter's transformation or metastasis from another primary malignancy. Renal cell carcinoma (RCC), on the other hand, is a malignancy which frequently metastasizes to bone and may cause an osteolytic solitary bone lesion. The origin of a solitary bone lesion in a patient with multiple potential primary malignancies has prognostic implications and affects treatment protocol, and as such must be diagnosed accurately. We describe a patient with CLL and a history of RCC who is found to have an incidental solitary bone lesion of the T11 vertebra. After two separate CT-guided biopsies revealed various lymphoid cell predominance and no evidence of RCC, treatment with low dose external beam radiation therapy (EBRT) was employed. Post-therapy MRI showed further propagation of the lesion. Surgical corpectomy was subsequently performed and postoperative pathology of the lesion was consistent with RCC. The patient was treated with bisphosphonates and a higher dose of EBRT. Our case illustrates the importance of surgical excisional biopsy for accurately diagnosing the primary source metastatic to the bone in a patient with CLL and another potential primary cancer.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".