Post-chemotherapy retroperitoneal lymph node dissection for non-seminomatous germ cell tumors: A single-surgeon, Canadian experience
Bibliographic record
Abstract
INTRODUCTION: Post-chemotherapy retroperitoneal lymph node dissection (PCRPLND) has a well-established role in the management of residual retroperitoneal masses >1 cm in patients with advanced non-seminomatous germ cell tumor (NSGCT). Herein, we report our single-surgeon surgical experience in a Canadian tertiary hospital. METHODS: We identified 57 patients with NSGCT who received primary chemotherapy and PCRPLND from 2010-2016. Surgical complication rate was graded with Clavien-Dindo classification. Chi-squared testing was used in testing for differences in proportion of PCRPLND tumor histology vs. the historical cohorts. Chi-squared testing was also used to analyze the association between primary orchiectomy tumor histology and post-chemotherapy residual mass (PCRM) tumor histology. RESULTS: The overall complication rate was 23% (n=13), of which four were Clavien-Dindo grade IIIb and one was grade IVa. Fourteen percent of patients required additional procedure for resection of adjacent organs intraoperatively. There was a statistically significant difference in the distribution of PCRPLND tumor histologies (Chi-squared p=0.0187), with a lower rate of viable tumor (7%) and higher rate of teratoma (63%) compared to historical cohorts. The absence of teratoma in the primary orchiectomy specimen was associated with the findings of fibrotic/necrotic tissue in the PCRM (Chi-squared p=0.0005). CONCLUSIONS: Our series demonstrated that the rate of viable tumor in PCRM appears lower than published historical series, and this possibly reflects the improvement in chemotherapy delivery in a contemporary series. The high rate of teratoma in the PCRM calls for ongoing need for PCRPLND. Grade III and IV surgical complications are considered rare in our series.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".