RECONSTRUCTIVE SURGERY FOR CHILDREN WITH PELVIC RHABDOMYOSARCOMA
Bibliographic record
Abstract
Rhabdomyosarcoma is the most common soft-tissue sarcoma found in children and can arise almost anywhere skeletal muscle is found.23 It represents 4% to 8% of malignant solid tumors in children, ranking behind central nervous system tumors, lymphoma, neuroblastoma, and Wilms' tumor.29 Genitourinary sites comprise approximately 20% of all pediatric rhabdomyosarcomas and represent a special subset of tumors arising from the bladder, prostate, paratesticular areas, vagina, uterus, and, rarely, the kidney and ureter.23 Historically, radical surgery, usually involving total pelvic exenteration, was used as first-line therapy, resulting in survival rates of approximately 30%.23 These relatively poor cure rates were associated with significant social and emotional long-term sequelae in children who survived the extensive surgery. Over the past 40 years, a dramatic change has occurred in the use of surgery in the overall treatment of rhabdomyosarcoma. In particular, cure rates are improved, and the role of radical surgery is diminished with advances in radiotherapy and with the development of more effective chemotherapeutic agents. Nonetheless, extirpative surgery still has an important role as an adjunctive treatment modality for genitourinary rhabdomyosarcoma. Reconstructive surgery has become an integral part of the total plan in patients undergoing radical surgery for rhabdomyosarcoma. Advances in surgical techniques can often provide a reasonable lifestyle for patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".