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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".