Laparoscopic approaches to adrenalectomy for large adrenal tumours: a systematic review
Bibliographic record
Abstract
Background: Laparoscopic adrenalectomy (LA) is considered the gold standard for benign, smaller secretory and non-secretory adrenal tumours. There is controversy however about its role for large tumors. In this review, we present the latest evidence on the feasibility of LA for large tumors and analyze the perioperative outcomes. Methods: Using PubMed database, we searched for relevant articles on LA for large tumors published between January 2010 and December 2021. These were scrutinized by two reviewers. Newcastle-Ottawa scale was used to assess the quality of the studies. Studies comparing robotic adrenalectomy with LA, duplicate data in multiple publications, case reports and articles focused on surgical technique or predictive factors were excluded. The outcomes analyzed included operative time, blood loss, surgical complications, average length of hospital stay, conversion to open surgery & tumor recurrence. A database was created on Microsoft ExcelTM. This is an unregistered systematic review. Results: A total of 15 suitable studies were identified. Retroperitoneal laparscopic adrenalectomy (RPLA) or transperitoneal laparoscopic adrenalectomy (TPLA) was found to be safe and effective for large tumours (≥5 cm) when compared to open adrenalectomy (OA). RPLA had shorter operative time, quicker return of bowel function and shorter length of hospital stay when compared to TPLA and OA. Hand assisted LA (HALA) discussed in one study concluded that complication rates were higher than TPLA but lower than OA. However, open conversion in HALA was lower compared to other TPLA studies. Conclusions: LA can be considered for large (≥5 cm) and potentially malignant tumors if the operators have the required technical skills and strict oncological principles are followed. The procedure should ideally be converted to open surgery if local invasion is suspected intraoperatively with potentially compromised resection margins. HALA is an excellent compromise in large tumours with anatomical and technical challenges. Limitations of this study include small sample size and limited number of well constructed prospective studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".