A Three-Variable Model Predicts Short Survival in Patients With Newly Diagnosed Metastatic Renal Cell Carcinoma
Bibliographic record
Abstract
BACKGROUND: Patients with metastatic renal cell carcinoma (mRCC) have variable survival outcomes. When discussing management approaches and providing information to patients and caregivers, it is important to have realistic perspectives, especially if the expected prognosis is very unfavorable. In the present study, factors predicting this endpoint were analyzed. METHODS: Data from 60 patients treated in routine clinical practice were evaluated. Unfavorable prognosis was defined as death within approximately 3 months from diagnosis of mRCC (maximum 3.5 months). Baseline factors including laboratory values and management approach were compared between the groups with short and longer survival. RESULTS: A total of 48 patients (80%) experienced ≥ 4 months survival (4+MS) and 10 (16.7%) experienced shorter survival (3MS). The others had short follow-up. Adverse prognostic factors that were significantly more frequent in the 3MS group were low hemoglobin, high lactate dehydrogenase and lack of systemic therapy. We used these three items to create a prognostic model: score 0 = no adverse factors, score 1 = one adverse factor, score 2 = two adverse factors, score 3 = three adverse factors. In the score 0 group, one out of 20 patients experienced 3MS (5%). In score 1, two out of 21 patients belonged to the 3MS group (9.5%). For score 2, the corresponding figure was four out of 14 patients (29%). In the score 3 group, three out of three patients experienced 3MS (100%) (P = 0.0001). CONCLUSIONS: A simple model with three prognostic factors predicted survival of patients with newly diagnosed mRCC. Additional validation in other databases is warranted.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".