Validation of a prediction model for avoiding post-chemotherapy retroperitoneal lymphadenectomy in patients with metastatic nonseminomatous germ cell cancer
Bibliographic record
Abstract
INTRODUCTION: Post-chemotherapy residual masses (PCRMs) may contain persistent cancer or teratoma in more than 50% of patients with metastatic non-seminomatous germ cell tumours (mNSGCTs). Retroperitoneal lymph node dissection (RPLND) is curative, but controversy exists about selection criteria for surgery. A validated prediction model by Vergouwe et al (2007) based on over 1000 patients was evaluated at our centre. METHODS: mNSGCT patients treated with RPLND for PCRMs were identified from an electronic database. Typographical errors in the model were identified and corrected using their 2003 publication, but retaining the 2007 coefficients. Six clinical variables were included in the model and the calculated probability of benign tissue was compared with pathology. "Benign tissue only" was considered a positive test outcome in patients with a predicted probability of "benign tissue only" greater than 70%. RESULTS: Fifty-two (52) mNSGCT patients between 1980 and 2014 were evaluable. Median age was 32 years (range 17-52) and International Germ Cell Consensus Classification (IGCCC) prognostic stages were: good 46.2%, intermediate 32.7%, and poor 21.2%. Most patients received bleomycin/etoposide/cisplatin (BEP) chemotherapy and full bilateral RPLND. Pathology showed residual cancer or teratoma in 31 patients (59.6%) and benign findings in 21 patients (40.6%). Positive and negative predictive values and accuracy were 100%, 69%, and 73%, respectively. CONCLUSIONS: "Benign tissue only" was found in 100% of patients in whom this was predicted using our pre-determined criteria. This study involved a limited number of patients, but confirms the potential value of the Vergouwe et al model. Routine use of this prediction model in clinical practice should be tested for mNSGCT patients with PCRMs.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".