Key Causes and Temporal Window of Unplanned Readmissions Following Transsphenoidal Pituitary Surgery: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Unplanned readmission (UR) after transsphenoidal pituitary surgery poses a significant healthcare burden, but its exact incidence, causes, and risk factors remain unclear. This meta-analysis aims to estimate the 30-day UR rate, identify its main causes, and explore associated risk factors. A systematic search of PubMed, Embase, Scopus, and Web of Science was conducted from inception to July 2025. Included studies reported 30-day UR rates and risk factors after transsphenoidal pituitary adenoma resection. Pooled incidence rates and odds ratios (ORs) with 95% confidence intervals (CIs) were derived using random-effects models. Heterogeneity was evaluated with I2 statistics, and study quality was assessed using the Newcastle-Ottawa scale. A total of 10 studies with 51,618 patients were included. The pooled 30-day UR rate was 9.68% (95% CI: 7.87–11.91). Hyponatremia (28.67%, 95% CI: 15.30–53.72) and cerebrospinal fluid leak (15.7%, 95% CI: 8.07–30.53) were the most common causes. Both all-cause and hyponatremia-related readmissions clustered early after surgery, with mean times of 9.96 days (95% CI: 6.51–13.42) and 8.05 days (95% CI: 5.86–10.25), respectively, suggesting a shared high-risk window around the first 2 weeks. No risk factor was reported in more than two studies, preventing meta-analysis of predictors. This meta-analysis confirms a high 30-day UR rate (nearly 10%) after transsphenoidal pituitary surgery, primarily due to hyponatremia and CSF leak. The aligned timing of readmissions underscores a critical post-discharge period. These findings emphasize the need for structured follow-up and early intervention—especially extended electrolyte monitoring and complication-specific management—to effectively reduce readmission risk.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.050 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".