C.02 Delayed new-onset hormone dysfunction following complete and incomplete resection of nonfunctioning pituitary adenomas
Bibliographic record
Abstract
Background: Post-operative delayed hormone dysfunction (DHD) in patients with nonfunctioning pituitary adenomas (NFPA) is highly variable and is predicted based on limited evidence. This study was undertaken to assess the likelihood of developing new DHD and its relation to the extent of tumor resection and recurrence. Methods: Four hundred fifty-five prospectively collected patient files were reviewed from our Program’s database. Inclusion criteria: NFPA; underwent surgery; and minimum follow-up of two years. Tumor recurrence was correlated with DHD (starting one year post-operatively) based on standardized annual imaging and hormone testing. Results: Eighty-nine patients met our inclusion criteria: 39 males and 50 females; mean follow-up was 4.3yrs (ranging from 2 to 11yrs). With no post-op residual tumor, the probability of developing DHD was only 7% by six years; no patient in this group developed DHD after three years of follow-up. In contrast, by six years, the probability of DHD was 33% in patients with residual stable tumor, and 54% in those with tumor recurrence/growth. Conclusions: By six years, approximately one third of patients with incomplete resection, and over half with tumor regrowth, will likely develop DHD. In contrast, the risk of DHD with complete tumor resection is <10% and, when seen, occurs within three years of surgery.
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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 | 0.000 |
| 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) | 0.003 | 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".