Current and future cancer staging after neoadjuvant treatment for solid tumors
Bibliographic record
Abstract
Until recently, cancer registries have only collected cancer clinical stage at diagnosis, before any therapy, and pathological stage after surgical resection, provided no treatment has been given before the surgery, but they have not collected stage data after neoadjuvant therapy (NAT). Because NAT is increasingly being used to treat a variety of tumors, it has become important to make the distinction between both the clinical and the pathological assessment without NAT and the assessment after NAT to avoid any misunderstanding of the significance of the clinical and pathological findings. It also is important that cancer registries collect data after NAT to assess response and effectiveness of this treatment approach on a population basis. The prefix y is used to denote stage after NAT. Currently, cancer registries of the American College of Surgeons' Commission on Cancer only partially collect y stage data, and data on the clinical response to NAT (yc or posttherapy clinical information) are not collected or recorded in a standardized fashion. In addition to NAT, nonoperative management after radiation and chemotherapy is being used with increasing frequency in rectal cancer and may be expanded to other treatment sites. Using examples from breast, rectal, and esophageal cancers, the pathological and imaging changes seen after NAT are reviewed to demonstrate appropriate staging.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.004 | 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".