Selected Common Diagnostic Problems in Urologic Pathology: Perspectives From a Large Consult Service in Genitourinary Pathology
Bibliographic record
Abstract
CONTEXT: Several common differential diagnoses are encountered in urologic pathology, frequently causing patient referrals for a second opinion. OBJECTIVES: To review 3 common differential diagnoses encountered in a large consultation service in genitourinary pathology, including partial atrophy versus prostatic acinar adenocarcinoma, oncocytoma versus chromophobe renal cell carcinoma, and urothelial carcinoma in situ versus normal urothelium and reactive atypia. We will discuss the detailed, morphologically distinctive features and the usefulness of immunohistochemistry. DATA SOURCES: Personal experience and review of the current literature. CONCLUSIONS: Careful morphologic assessment and awareness of diagnostic pitfalls are fundamental in reaching a definitive diagnosis in most cases. Immunohistochemistry is useful but should be used only in conjunction with the morphologic impression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".