Utilization of SiPAP Classification in Prediction of Pituitary Adenoma Recurrence: The Ottawa Hospital Experience
Bibliographic record
Abstract
Introduction: Pituitary adenomas are a common skull base tumor that varies in size, often classified based on function and size, either micro- or macroadenoma. Patient clinical presentation depends on multiple factors, including excessive hormone secretion, the mass effect of large tumors, and tumor invasion to surrounding structures. With the advancement of diagnostic modalities, MRI is considered being the modality of choice to evaluate pituitary tumor features including parasellar extension. Endoscopic endonasal transsphenoidal (EETS) resection is the surgical standard of care for pituitary adenoma resection due to its superior visualization of the sella and surrounding anatomy. However, recurrence of the pituitary tumor following surgery has been reported widely. Yet, intuitively, adenoma size and involvement of parasellar structures should impact gross tumor resection (GTR) and recurrence. We evaluated a modified score using the SIPAP classification system, combining the suprasellar and parasellar extension scores of the pituitary tumor to determine its impact on adenoma recurrence.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".