Bibliographic record
Abstract
Olfactory neuroblastoma (ONB) is a malignant neuroectodermal tumor that typically occurs in the superior nasal cavity. It is a distinct entity with features that include nesting, low-grade stippled nuclei, and neurofibrillary stroma with formation of pseudorosettes. It has a distinctive immunoprofile that includes keratin negativity, neuroendocrine marker positivity, and S100 positive sustentacular cells, which surround the nests of tumor in a supportive manner. Although the typical clinicopathologic features leave little room for misinterpretation, the wide variability in this tumor may cause diagnostic difficulty. This includes immunophenotypic diversity or patchy staining with immunomarkers, wide spectrum of grade and histology, posttreatment changes, and occasional divergent differentiation. In addition, problems in sampling, preservation, and clinical localization may make the diagnosis more challenging. A large group of tumors may show morphologic overlap with ONB, with some mimicking low-grade tumors, whereas others mimic high-grade tumors. This differential diagnosis has important treatment and prognostic implications. Further complicating the issue is that many of the mimics themselves may show great morphologic and immunophenotypic variability. This manuscript will review the typical clinical and pathologic features of ONB, address the great variability of ONB in our experience, will focus on the differential diagnosis and report on recent findings in these tumors including molecular genetics where applicable.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".