Hemipelvectomy: a changing perspective for a rare procedure.
Bibliographic record
Abstract
OBJECTIVE: To compare the prognosis of patients undergoing a hemipelvectomy (HP) in the treatment of pelvic sarcomas and carcinomas and to review the morbidity and mortality associated with HP. DESIGN: Retrospective chart review. SETTING: The Foothills Hospital, University of Calgary, Calgary, Alberta. PATIENTS: Thirteen patients with clinically and radiographically isolated malignancies involving the bony pelvis and adjacent structures. INTERVENTIONS: Patients were treated with either an external HP (9 patients) or internal HP (4) in 1983-2001. OUTCOME MEASURES: Survival and recurrence rates for patients in 2 histopathologic groups (sarcoma v. carcinoma); morbidity and mortality associated with HP. RESULTS: Hemipelvectomy was performed for 7 sarcomas (4 primary bone and 3 soft tissue) and 6 carcinomas (5 genital tract and 1 unknown primary). Seven of the 9 external HPs involved composite resection of other pelvic structures, including other pelvic viscera (3 patients), sacrum (3) and portions of lumbar vertebrae and nerves (1). There were no additional resections among the 4 internal HPs, but 3 patients had allograft reconstruction. Length of stay averaged 30 days (range 14-70 d). At least 1 complication occurred in 10 of 13 cases. The most common complication was flap necrosis occurring in 5 patients (38%). There was 1 perioperative death (8%). The survival of patients treated for sarcomas was better than for carcinomas, which were primarily of the genital tract. Only 1 of the patients with a pelvic sarcoma died of disease (86% disease-specific survival), with a median follow-up of 12 months (range 9-108 mo). Of the 7 sarcoma patients 5 were disease-free at last follow-up. One of 6 pelvic carcinoma patients died perioperatively, with another dying of unknown causes 4 months after surgery. Of the 4 remaining patients 3 died of disease, resulting in a median survival of 9 months (range 4-20 mo). Four of 6 patients with pelvic carcinomas developed recurrent disease, none local. CONCLUSIONS: HP has considerable morbidity but is a viable and potentially curative treatment for patients with pelvic sarcomas. With pelvic carcinomas HP was not curative, but did provide short-term local disease control. Future improvements in imaging techniques and quality-of-life studies may help with patient selection. The role of HP in recurrent carcinoma remains to be determined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".