Accuracy capabilities comparisons between Karakiewicz, Kattan and Cindolo nomograms in predicting outcomes for renal cancer carcinoma: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Several prognostic models have been proposed to predict outcomes of patients affected by renal cell carcinoma. We analyze the discriminative capabilities of Karakiewicz, Kattan and Cindolo nomograms and perform a meta-analysis to yield pooled area under the receiver operator curves (AUCs) for model comparison. The end points of interest were disease-recurrence free survival (DFS) and cancer-specific survival (CSS). METHODS: An electronic search of the Medline and Embase was undertaken until July 2014. The AUC value, total number of patients, number of disease recurrence, and cancer-related deaths were extracted from the included references. AUCs of the models were converted to odds ratios (ORs). For the meta-analysis, ln(OR) was used for data pooling. For each nomogram, the combined OR was transformed back to a converted AUC (cAUC). RESULTS: A total of 16 studies were identified including 26 710 patients. The derived comparison of cAUC values revealed better predictive capability of DFS for the postoperative Karakiewicz nomogram versus Kattan nomogram (p < 0.01), but not versus Cindolo (p = 0.432) and between Cindolo versus Kattan (p = 0.03). The Mantel-Haenszel derived comparison of cAUC values revealed better predictive capability for the preoperative Karakiewicz nomogram versus the Kattan nomogram (p < 0.01) and versus the Cindolo model (p < 0.01), but also between the postoperative Karakiewicz model versus the Kattan model (p < 0.01) and the Cindolo model (p < 0.01). The Kattan model showed better discriminative capability versus the Cindolo model (p < 0.01). CONCLUSIONS: The predictive abilities of the pre- and postoperative Karakiewicz models are higher than Kattan or Cindolo in predicting DFS and CSS.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".