P.096 Practice patterns in the management of residual/recurrent non-functioning pituitary adenomas: results from a Canada-wide survey
Bibliographic record
Abstract
Background: Postoperative follow-up of non-functioning pituitary adenomas (NFAs) occasionally detects residual or recurrent disease. Subsequent treatment options range from continued follow-up, to re-resection or radiotherapy. To better understand current practice patterns on this topic, we surveyed neurosurgeons and radiation oncologists in Canada. Methods: Skull-base neurosurgeons and radiation oncologists across Canada were invited to complete a 25-item online questionnaire. Summary statistics were computed and 2-tailed t-tests were performed to assess significance. Results: 33 participants returned completed questionnaires: neurosurgeons (n=20, 61%) and radiation oncologists (ROs; n=13, 39%). When treating giant (>3cm) tumours, 92% of neurosurgeons in practice for less than 15 years use an endoscopic approach, as compared to only 57% of neurosurgeons in practice for 15 years or more. Additionally, younger neurosurgeons have a greater tendency to advocate for stereotactic radiosurgery (SRS) or re-resection (54% and 38%, respectively), as compared to older surgeons who show a higher propensity (29%) to advocate for observation. The presence of cavernous sinus extension appears to encourage neurosurgeons (40%) to offer radiotherapy sooner, as compared to 62% of ROs. Conclusions: Our results identify both variations and commonalities in practice amongst Canadian neurosurgeons. Approaches deviate in the setting of residual tumors based on years of practice.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".