Patients with distant metastases from renal cell carcinoma can be accurately identified: external validation of a new nomogram
Bibliographic record
Abstract
OBJECTIVE: To identify clinical variables that can accurately predict the presence of distant metastases in patients with renal cell carcinoma (RCC). PATIENTS AND METHODS: Age, symptom classification, tumour size and the prevalence of distant metastases at diagnosis before nephrectomy were available for 5376 patients with pathologically confirmed RCC. The data of 2660 (49.5%) patients from 11 centres were used to develop a multivariable logistic regression model-based nomogram predicting the individual probability of distant metastases. The remaining data from 2716 (50.5%) patients from three institutions were used for external validation. RESULTS: In the development cohort, 269/2660 (10.1%) had distant metastases, vs 285/2716 (10.5%) in the external validation cohort. Symptom classification and tumour size were independent predictors of distant metastases in the development cohort; age was not an independent predictor. A nomogram based on symptom classification and tumour size was 85.2% accurate in predicting the individual probability of distant metastases in the external validation cohort. CONCLUSION: Although distant metastases might be easily identifiable in some patients, their diagnosis might be a challenge in others. The current nomogram provides a simple, user-friendly and, most importantly, an accurate tool aimed at predicting the probability of distant metastases in patients with RCC.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".