Melanoma of the sellar region mimicking pituitary adenoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We report here the case of an 82-year-old woman who presented with visual disturbance. MRI demonstrated a sellar mass. The diagnosis of pituitary adenoma was made. She underwent transnasal surgery. Histologic, immunohistochemical and ultrastructural studies indicated that the tumor was a melanoma. Despite an exhaustive search for a primary lesion elsewhere, none was found. The sellar tumor was considered a primary lesion, although extrasellar primary tumor imaging cannot be excluded with 100% certainty. Reported examples of melanoma affecting the sellar region are few. They exhibit morphologic features identical to those of melanomas arising elsewhere. Although very rare, primary melanomas enter into the differential diagnosis of sellar lesions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it