Laparoscopic adrenalectomy: pathologic features determine outcome.
Bibliographic record
Abstract
INTRODUCTION: The differential outcomes of laparoscopic adrenalectomy are not well described. Therefore, we evaluated these outcomes in the 3 groups most often seen clinically: bilateral adrenalectomy for Cushing's disease (group 1), pheochromocytoma (group 2) and unilateral adrenalectomy for non-pheochromocytoma (group 3). METHODS: We reviewed a longitudinal database of 72 consecutive cases of laparoscopic adrenalectomy carried out between 1997 and 2001 at the Centre for Minimally Invasive Surgery, University of Toronto. RESULTS: Patients in group 1 tended to be older (median 49 yr) and heavier (median 87 kg). They had a longer operating time (median 255 min), more postoperative complications (15%) and a longer median postoperative stay (4 d). Patients in group 2 had intermediate outcomes: a median operating time of 198 minutes, complication rate of 8.3% and a median postoperative hospital stay of 3 days. However, they had more intraoperative blood loss (median 150 mL). Group 3 patients had the best outcomes with the shortest median operating time (125 min), least blood loss (median 50 mL), fewer complications (6%) and shortest hospital stay (median 2 d). CONCLUSIONS: Although the outcomes of laparoscopic adrenalectomy are uniformly good, on the basis of the underlying pathologic characteristics, patients can be divided into groups that have different expected outcomes. Patients requiring a unilateral adrenalectomy except for pheochromocytoma have the best recorded outcomes. Surgeons transferring to laparoscopic adrenalectomy would benefit from selecting patients in this group during their learning curve.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".