P.089 Recovery from chronic secondary adrenal insufficiency in patients with pituitary disorders
Bibliographic record
Abstract
Background: Patients with pituitary disorders may be placed on steroid replacement for secondary adrenal insufficiency (SAI), generally after pituitary surgery; however, data regarding recovery of long-term SAI are lacking. We conducted a study to assess the longer term recovery rate of SAI in patients with pituitary disorders. Methods: We identified all SAI patients from prospectively entered data in the Halifax Neuropituitary Database from November 1, 2005 to September 30, 2014, who had required glucocorticoid therapy for >3 months, and a minimum follow-up of 6 months. Exclusion: ACTH-secreting adenomas; peri-operative glucocorticoid treatment only; glucocorticoids for non-pituitary conditions. Results: 55 patients fulfilled the criteria, 41 (75%) of which had transsphenoidal surgery. Nine (16.4%) patients had complete recovery of SAI over a median of 20 months (range: 8–51). Smaller tumour size and initial cortisol >175 nmol/L had increased likelihood of recovery; those with secondary hypogonadism or growth hormone deficiency were less likely to recover. Conclusions: This is the first study to examine long-term recover of SAI in patients with pituitary disorders: approximately 1 in 6 patients recover adrenal function, up to 5 years after diagnosis. Consequently, patients with SAI should undergo regular testing to prevent unnecessary chronic glucocorticoid therapy.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".