P.040 New-onset secondary hormone deficiency in patients with incidental versus clinically manifesting sellar masses
Bibliographic record
Abstract
Background: Secondary hormonal deficiency (SHD) in patients with sellar masses (SM) is associated with significant morbidity. Purpose: to compare long-term risk of new-onset SHD in SM found incidentally (ISM) versus those clinically manifesting (CMSM). Methods: From the Halifax Neuropituitary Program’s database, we identified all patients having non-functioning and non-pituitary SM from January 1, 2006, with ≥ 12 months follow-up. Results: There were 214 CMSM (108 with baseline SHD) and 148 ISM (37 with baseline SHD) patients (mean follow-up: 5.7 and 5.0 years, respectively). In patients who underwent early surgery (<90 days from diagnosis), 3-month post-op hormonal function was considered baseline. Despite unchanged tumour size in over 95%, 129 (35.6%) developed new-onset SHD at up to 120 months. The risk of developing new-onset SHD was similar in CMSM and ISM groups (HR = 1.10; CI= 0.69-1.75; p= 0.7), and in surgical and nonsurgical patients (HR=1.24; CI= 0.59-2.61; p = 0.58). Conclusions: More than one third of patients with non-functioning or non-pituitary SM, presenting either with clinical manifestations or as incidental lesions, will develop new-onset SHD. Furthermore, SHD may develop several years later and despite stability of tumors, highlighting the need for ongoing, long-term hormonal assessment.
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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 | 0.000 | 0.002 |
| 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".