Open adrenalectomy for medium sized adrenocortical tumour: How I do it?
Bibliographic record
Abstract
INTRODUCTION: The aim of our work was to report our experience in managing cases with medium-sized adrenocortical carcinoma by the high retroperitoneal extra pleural approach. METHODS: During the past 2 years, 10 patients with suspected adrenocortical carcinoma were managed by our technique: the high supra 10th rib, retroperitoneal extra pleural approach. We included cases with 5 to 10 cm adrenal masses, suspected as adrenocortical carcinoma. RESULTS: The mean patient age was 38 years (range: 26-44), the median tumour volume was 7 cm (range: 5-8). Of the 10 patients, 7 were female. Of the patients, 6 had right- and 4 had left-sided tumours. Intraoperatively, all cases had proper surgical removal, with no apparent residual tumour tissue. No single patient required a chest tube or developed respiratory problems. There were no major vascular injuries during surgery. We did not compare our findings to the standard lateral or subcostal approaches, as in our institution we adopt this high lateral approach for medium-sized tumours, while managing larger tumours with transperitoneal subcostal approach and smaller tumours laparoscopically. CONCLUSION: The high supra 10th lateral retroperitoneal, extra pleural approach is a safe, doable technique, allowing easy access to medium-sized suprarenal tumours and its vasculature, for cases suspected to be adrenocortical carcinoma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".