Practice patterns in the management of recurrent and residual non-functioning pituitary adenomas: Results from a Canada-wide survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it