The diagnosis and treatment of primary adrenal lipomatous tumors in Chinese patients: a 31-year follow-up study
Bibliographic record
Abstract
INTRODUCTION: Adrenal lipomatous tumours (ALTs) are rarely encountered in clinical practice and consequently little is known about their clinical features. METHODS: We analyze the clinical features, diagnosis and treatment of ALTs based on cases presenting at a single centre over a 31-year period. We reviewed clinical data from patients with primary adrenal tumours treated at the Ruijin Hospital, Shanghai between January 1980 and December 2010. RESULTS: A total of 73 cases of primary ALTs in 22 men and 51 women (mean age 51.1±14.2 years) were reviewed. The ALTs included 65 myelolipomas (89.0%), 3 lipomas (4.1%), 2 angiomyolipomas (2.7%), 2 teratomas (2.7%), and 1 liposarcoma (1.4%). Of the total 73 patients, 24 of them had tumours in the left adrenal gland, 47 in the right gland and 2 had bilateral tumours. In total, 51 patients underwent open surgery and 22 laparoscopic surgery. CONCLUSION: Myelolipoma is predominant among the various types of lipomatous adrenal gland tumours; it accounts for about 90% of all cases. Surgery is recommended for tumours ≥3.5 cm in diameter, for all cases of symptomatic tumour, and for cases of teratoma or liposarcoma identified by preoperative imaging.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".