Increased Pre-Operative Lung Immune Prognostic Index Score Is a Prognostic Factor in Cases of Pathological T3 Renal Cell Carcinoma
Bibliographic record
Abstract
We assessed the efficacy of the lung immune prognostic index (LIPI) in predicting the progression of pathological T3 renal cell carcinoma (RCC). The LIPI scores of patients with pathological T3 RCC were calculated in the pre- and post-operative phases. Patients were divided into zero-point, one-point, and two-point groups according to their LIPI score and into the upstage and non-upstage groups according to the pre- and post-operative increase in LIPI score. Overall survival (OS) was evaluated using Kaplan–Meier curves stratified by group. Univariate and multivariate analyses of OS were performed via Cox proportional hazard regression analysis. LIPI scores were calculated in 80 patients wherein blood sampling data were available. The upstage and non-upstage groups comprised eight and seventy-two patients, respectively. Kaplan–Meier curves showed a significant difference in the pre- to post-operative LIPI score upstage group. LIPI score change was a poor prognostic factor using univariate analysis (OS: hazard ratio (HR) = 4.10, 95% confidence interval (CI) = 1.07–15.61, p = 0.038) and multivariate analysis (OS: HR = 4.38, 95% CI = 1.13–16.89, p = 0.031). An increase in the LIPI score in the pre-operative phase was a poor prognostic factor for pathological T3 RCC.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".