P.056 Predictability of pituitary tumor resection and recurrence following endoscopic endonasal trans-sphenoidal surgery
Bibliographic record
Abstract
Background: The surgical treatment of pituitary tumour has undergone substantial changes over time. In this study we evaluated our institutional results for pituitary tumour surgery using the endoscopic endonasal trans-sphenoidal (EETS) approach. Methods: Patient demographic, clinical and surgical data were extracted from medical records. Preoperative MRI images were reviewed. The SIPAP classification was applied to the pituitary tumors. Chi2 test and t test were used for statistical analysis. Results: 202 cases were identified. Functional tumors were present in 29% of the cohort. Patients with a suprasellar or parasellar SIPAP score of 0 or 1 had complete resection of their tumor in 66.6% of cases, compared to 29% with a suprasellar or parasellar SIPAP score ≥ 2 (Risk Ratio 2.3 CI 1.58-3.39, p=0.0005). When the tumor was completely resected radiologically, the mean time to recurrence was not different for the SIPAP 0 or 1 group which was 27 months in comparison to 34 months for the group with a SIPAP score 2 (p=0.13). Conclusions: Our study results showed that the preoperative MRI SIPAP score can be used to better inform patients about their expected outcomes of EETS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".