A critical assessment of the prognostic value of clear cell, papillary and chromophobe histological subtypes in renal cell carcinoma: a population‐based study
Bibliographic record
Abstract
OBJECTIVE To assess the magnitude of the effect of histological subtype (HS, the three most common being clear cell, papillary and chromophobe) on cause-specific mortality (CSM) from renal cell carcinoma (RCC). PATIENTS AND METHODS Univariable and multivariable Cox regression models included data from 11 618 patients treated with nephrectomy between 1988 and 2004 in nine Surveillance Epidemiology and End Results registries. We tested whether HS represents an independent predictor of CSM, and whether HS adds to the ability of other variables to predict CSM. The covariates comprised age, year of surgery, T stage, nodal status, M stage and Fuhrman grade. RESULTS In a multivariable model predicting CSM, HS was an independent predictor (P = 0.03), but failed to improve the accuracy of the model (+0.1% gain when HS was included in the model). CONCLUSION Although we confirmed that HS is an independent predictor for CSM, there was no gain in accuracy when HS was added to standard predictors of CSM. From a practical perspective, this implies that patients with clear cell, papillary and chromophobe HS share similar natural histories after nephrectomy, provided that other cancer characteristics are accounted for. From a statistical perspective, in multivariable models of CSM, the clear cell, papillary and chromophobe HS might be included as a single entity.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".