Patients with renal cell carcinoma nodal metastases can be accurately identified: External validation of a new nomogram
Bibliographic record
Abstract
Outcome of patients with renal cell carcinoma nodal metastases (NM) is substantially worse than that of patients with localized disease. This justifies more thorough staging and possibly more aggressive treatment in those at risk of or with established NM. We developed and externally validated a nomogram capable of highly accurately predicting renal cell carcinoma NM in patients without radiographic evidence of distant metastases. Age, symptom classification, tumour size and the pathological nodal stage were available for 4,658 individuals. The data of 2,522 (54.1%) individuals from 7 centers were used to develop a multivariable logistic regression model-based nomogram predicting the individual probability of NM. The remaining data from 2,136 (45.9%) patients from 5 institutions were used for external validation. In the development cohort, 107/2,522 (4.2%) had lymph node metastases vs. 100/2,136 (4.7%) in the external validation cohort. Symptom classification and tumour size were independent predictors of NM in the development cohort. Age failed to reach independent predictor status, but added to discriminant properties of the model. A nomogram based on age, symptom classification and tumour size was 78.4% accurate in predicting the individual probability of NM in the external validation cohort. Our nomogram can contribute to the identification of patients at low risk of NM. This tool can help to risk adjust the need and the extent of nodal staging in patients without known distant metastases. More thorough staging can hopefully better select those in whom adjuvant treatment is necessary. (c) 2007 Wiley-Liss, Inc.
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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 | 0.012 | 0.032 |
| 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) | 0.000 | 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".