Characterization of the Cortisol Withdrawal Syndrome after Successful Cushing’s Disease Surgery
Bibliographic record
Abstract
Background: Successful surgery for Cushing’s disease (CD) leads to a sudden change in cortisol levels and patients often experience a constellation of symptoms consistent with the abrupt change in cortisol levels, a condition we term cortisol withdrawal syndrome (CWS). The goal of this study was to characterize CWS by examining changes in quality of life (QoL) before and after successful surgery. Methods: Using a CD-specific questionnaire we developed, changes in QoL in the early postoperative period (2.6 ± 1.2 months), compared to before surgery, were investigated in 33 CD patients who underwent successful surgery. Item- and domain-level changes in QoL were investigated using t -tests and correlation matrices. A Euclidean hierarchical analysis was used to identify latent subgroups of QoL changes. Linear regressions tested the effect of follow-up duration, BMI, and preoperative cortisol levels on changes in QoL. Results: Worsened appetite was the only significant item-level change in the early postoperative group ( t (32) = −2.39, p = 0.023). Changes in the “stretch marks” item correlated with changes in the “facial hair” item ( r (31) = 0.70, 95% CI [0.46–0.84], p < 0.001). Changes in the physical health domain were significantly correlated with changes in emotional health ( r (31) = 0.56, 95% CI [0.27–0.76], p < 0.001) and general health ( r (31) = 0.52, 95% CI [0.21–0.73], p = 0.002). The cluster analysis yielded six distinct subgroups. QoL worsened across all domains except social well-being, especially mental status, in the largest subgroup ( n = 8, 24%). Conversely, the smallest subgroup ( n = 2, 6%) experienced pronounced improvements across almost all domains. The four remaining subgroups experienced moderate changes in distinct combinations of QoL domains. Discussion: Our study is the first to investigate associations between changes in the items and domains that comprise QoL in this patient group. Furthermore, we describe novel subgroups of QoL changes after successful CD treatment. Future studies should investigate the clinical factors that characterize these subgroups. Thus, large-sample longitudinal studies are needed to investigate the detailed QoL changes caused by CD. Publication History Article published online: 07 February 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".