Radiographic predictors of occult intracranial involvement in olfactory neuroblastoma patients
Bibliographic record
Abstract
BACKGROUND: Traditional management of olfactory neuroblastoma (ONB) includes margin-negative resection with removal of cribriform plate, dura, and olfactory bulb, regardless of intracranial disease. This approach may be overtreating certain patients. Our investigation examines risk factors associated with occult intracranial disease to optimize therapeutic outcomes. METHODS: This retrospective, multi-institutional cohort study examined clinical covariates associated with occult intracranial involvement. Patient demographics, staging, Hyam's grade, and pathologic involvement of dura, olfactory bulb/tract, and brain were collected. Diagnostic imaging was reviewed. Positive and negative predictive value (NPV) were estimated along with effect size estimates. Cox hazard regression examined associations with overall survival (OS) and disease-free survival (DFS). RESULTS: = 0.26; 95% confidence interval [CI]: 0.16, 0.42). Subjects without radiologic skull base involvement (n = 66) had pathologic positivity of 12.1%. Within this subgroup, Hyam's grade was clinically significant for dural positivity (φ = 0.34; 95% CI: -0.12, 0.71) with 28.6% involvement in high grade tumors. Neither clinical nor pathologic positivity of intracranial structures were associated with significantly different OS or DFS. CONCLUSIONS: Both CT and magnetic resonance imaging (MRI) had reasonably good NPV for involvement of dura and olfactory bulb. Higher Hyam's grade was associated with dural involvement. Patients with low-grade tumors not involving the skull base may be suitable for avoiding skull base resection; however, further investigation is warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".