TNM residual tumor classification revisited
Bibliographic record
Abstract
BACKGROUND: For cancer patients, prognosis is strongly influenced by the completeness of tumor removal at the time of cancer-directed surgery or disease remission after nonsurgical treatment with curative intent. These parameters define the relative success of definitive treatment and can be codified by an additional subclassification within the TNM system, the residual tumor (R) classification. Despite the importance of residual tumor status in designing clinical management after treatment, misinterpretation and inconsistent application of the R classification frequently occur that diminish or abrogate its clinical utility. METHODS: An analysis of the relevant literature regarding the use and prognostic importance of the R classification was undertaken. RESULTS: In the current study, the prognostic importance of the R classification for different kinds of tumors is discussed. Problems that arise in using the R classification are described. Special issues regarding the use of the R classification are addressed. CONCLUSIONS: The R classification is a strong indicator of prognosis and facilitates the comparison of treatment results if applied in a consistent manner. Uniform use and interpretation of this classification is essential for the standardization of posttreatment data collection.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".