Lymph Node-Based Prognostics: Limitations With Individualized Cancer Treatment
Bibliographic record
Abstract
OBJECTIVES: Clinicians will commonly individualize adjuvant cancer therapy, on the basis of the number of involved lymph nodes and other clinicopathological factors, under the assumption that despite the expected statistical variability of such data one can nonetheless garner useful information for the individual case. Here the scientific basis of this assumption will be examined. METHODS: Survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program for 19,107 breast, 4,234 gastric, and 4,058 rectal cancers were studied with Kaplan-Meier estimates and Cox proportionate hazard models. The minimal sample size required to discriminate between high and low-risk groups was determined from the hazard ratios between various comparative groups, and their respective frequencies. RESULTS: The number of involved nodes was the strongest prognostic factor for all 3 cancers, followed by tumor diameter and grade. Discrimination between high and low-risk nodal prognostic groups required samples of 30 to 200 cases, depending on the prognostics used and the specific tumor, to attain a two-sided alpha of 0.05% with 90% power. At the individual level such prognostications therefore were uninformative. CONCLUSIONS: Clinicopathological prognostics based upon the number of involved lymph nodes are subject to population heterogeneity that limits their application to large samples. At the individual level, these prognostics appear more spurious than useful. The use of such prognostics to tailor cancer treatment to individuals should be considered a specious practice; instead a more categorical approach, based on the results of randomized trials, should be used.
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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 | 0.081 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".