P.168 Prediction of Pituitary Adenoma Recurrence using the SIPAP Classification
Bibliographic record
Abstract
Background: Pituitary tumor recurrence following endoscopic endonasal transsphenoidal surgery (EETS) has been reported widely. We evaluated a modified score using the SIPAP classification system, combining the suprasellar and paraseller extension scores of the pituitary tumor, to determine its impact on adenoma recurrence. Methods: A retrospective cohort study design with patient characteristics, tumor type, endocrine, operation, imaging data collected. Preoperative MRI images were reviewed and SIPAP classification applied. Postoperative data were extracted for the follow-up period available for each patient.The suprasellar score and the highest parasellar scoring from both sides were numerically summed in a bilateral suprasellar and parasellar (SaP) score and combined to make 4 grades. Results: 276 patients were identified, 56.5% of the cohort was male. The mean cohort age was 54 years old.The mean follow up period was 32 months. Patient perioperative tumor grade according to SaP classification and recurrence rate was: Grade 1: 11%: Grade 2: 10%; Grade 3: 15%; Grade 4: 22%. The results followed a pattern of logarithmic curve. Conclusions: The SaP classification was useful in determining the pituitary tumor expected recurrence following EETS. The advanced tumors had the highest recurrence rates. Use of the SaP score may allow for more accurate preoperative counselling of patients with pituitary adenoma.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".