Practice patterns in the management of recurrent and residual non-functioning pituitary adenomas: Results from a Canada-wide survey
Bibliographic record
Abstract
BACKGROUND: There is no consensus regarding the management and postoperative follow-up of non-functioning pituitary adenomas (NFAs) in the setting of recurrent or residual disease. Subsequent treatment options include continued follow-up, re-resection or radiotherapy. To address this gap and better understand current practice patterns, we surveyed neurosurgeons and radiation oncologists in Canada. METHODS: Neurosurgeons and radiation oncologists (ROs) across Canada were invited to complete a standardized online questionnaire. Summary statistics were computed, and Fisher's Exact tests were performed to assess significance. Qualitative analyses were performed through open and axial coding. RESULTS: = 13). When treating giant (>3 cm) tumors, 90.9% of neurosurgeons in practice for less than 10 years reported using an endoscopic approach, as compared to only 66.7% of neurosurgeons in practice for 10 years of more. Additionally, neurosurgeons who were newer to practice had a greater tendency to advocate for stereotactic radiosurgery (SRS) or re-resection (54.5% and 36.4%, respectively), as compared to older surgeons who showed a higher propensity (22.2%) to advocate for observation. The presence of cavernous sinus extension appeared to encourage ROs to offer radiotherapy sooner (61.4%), as compared to 40% of neurosurgeons. CONCLUSIONS: Our results identified both variations and commonalities in practice amongst Canadian neurosurgeons. Approaches deviated in the setting of residual tumor based on years of practice. This work provides a critical foundation for future studies aiming to define evidence-based best practices in the management of NFAs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".