Laparoscopic Adrenalectomy: Tailoring Approaches for the Optimal Resection of Adrenal Tumors
Bibliographic record
Abstract
In this study, we investigated the outcomes of laparoscopic approaches for adrenal tumor resection in 67 patients from a single center with a median age of 51 (range 40–79). Predominantly comprising women, the majority of patients were overweight or obese. Adrenal tumors larger than 6 cm were mostly treated using the laparoscopic transperitoneal method (p < 0.001). Our results revealed that patients subjected to the retroperitoneal approach exhibited quicker recovery, as evidenced by faster resumption of oral intake and ambulation, along with reduced intraoperative blood loss and shorter hospitalization (p-value < 0.05). In contrast, patients subjected to the transperitoneal approach experienced minimal complications, though not statistically significant, despite the technique’s intricacy and slower recovery. These findings emphasize the significance of tailoring the surgical approach to individual patient characteristics, with particular emphasis on the tumor size. The choice between the retroperitoneal and transperitoneal methods should be informed by patient-specific attributes to optimize surgical outcomes. This study underscores the need for a comprehensive evaluation of factors such as tumor characteristics and postoperative recovery when determining the most suitable laparoscopic approach for adrenal tumor resection. Ultimately, the pursuit of individualized treatment strategies will contribute to improved patient outcomes in adrenal tumor surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".