Recovery Room Cortisol Predicts Long-Term Glucocorticoid Need After Transsphenoidal Surgery for Pituitary Tumors
Bibliographic record
Abstract
BACKGROUND: Accurate assessment of the need for glucocorticoid therapy is essential after transsphenoidal surgery (TSS) for pituitary tumors. Agreement on the best test to use in the early postoperative setting is lacking. OBJECTIVE: To examine recovery room (RR) cortisol as a predictor of long-term need for glucocorticoids. METHODS: We conducted a retrospective cohort study of 149 patients who underwent TSS for pituitary tumors between January 2007 and December 2014. Pathological tumor diagnoses were confirmed. Endocrinologists assessed the need for glucocorticoid supplementation within 6 to 8 wk after TSS. We extracted data on preoperative, RR, and day 1 to 3 post-TSS morning serum cortisol (MSC). We reported areas under the receiver operating characteristic curve (AUC) and diagnostic measures for different cortisol measures. We also conducted a logistic regression to identify the most predictive variables. RESULTS: Eighteen patients required glucocorticoid supplementation at follow-up. RR cortisol was the most accurate measurement in the early postoperative period (AUC [95% confidence interval (CI)], .92 [.85-.99]; P < .001), followed by day 1, 2, and 3 post-TSS MSC, respectively. A threshold RR cortisol of 744.0 nmol/L (26.97 μg/dL) had 90.9% sensitivity and 73.7% specificity for detecting patients in the hypocortisolism group, while 757.5 nmol/L (27.46 μg/dL) had 100% and 70.0%, respectively. The logistic regression identified RR cortisol as the sole significant predictor (odds ratio [CI], .36[.18-.71] for every 100 nmol/L increase; P = .0033). CONCLUSION: The RR cortisol is accurate in predicting long-term glucocorticoid supplementation and may be the best early postoperative measure. Future larger studies should validate these findings and derive optimal RR cortisol threshold values.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".